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Tag #mining

2030 papers:

PADLPADL-2020-ShakerinG #induction #using
Whitebox Induction of Default Rules Using High-Utility Itemset Mining (FS, GG), pp. 168–176.
PADLPADL-2020-TarauB #graph #interactive #prolog
Interactive Text Graph Mining with a Prolog-based Dialog Engine (PT, EB0), pp. 3–19.
EDMEDM-2019-AulckNVBW #predict
Mining University Registrar Records to Predict First-Year Undergraduate Attrition (LSA, DN, NV, JB, JW).
EDMEDM-2019-YahyaMO #data mining #education #effectiveness #novel #source code #using
A Novel Use of Educational Data Mining to Inform Effective Management of Academic Programs (AAY, FAM, AO).
ICPCICPC-2019-MashhadiH #algorithm #empirical #specification
An empirical study on practicality of specification mining algorithms on a real-world application (MJM, HH), pp. 65–69.
ICPCICPC-2019-SilvaR0SPM #programming #recommendation
Recommending comprehensive solutions for programming tasks by mining crowd knowledge (RFGdS, CKR, MMR0, KAS, KVRP, MdAM), pp. 358–368.
MSRMSR-2019-CamposSMB0 #javascript
Mining rule violations in JavaScript code snippets (UC, GS, JPM, RB, GP0), pp. 195–199.
MSRMSR-2019-ChatterjeeDPAK #case study #re-engineering #tool support
Exploratory study of slack Q&A chats as a mining source for software engineering tools (PC, KD, LLP, VA, NAK), pp. 490–501.
MSRMSR-2019-DiamantopoulosS #evolution #stack overflow #towards
Towards mining answer edits to extract evolution patterns in stack overflow (TD, MIS, ALS), pp. 215–219.
MSRMSR-2019-GaoKLBK #android
Negative results on mining crypto-API usage rules in Android apps (JG, PK, LL0, TFB, JK), pp. 388–398.
MSRMSR-2019-GoteSS #git #named #network #repository #scalability
git2net: mining time-stamped co-editing networks from large git repositories (CG, IS, FS), pp. 433–444.
MSRMSR-2019-KolovosNBMP #distributed #framework #named #repository
Crossflow: a framework for distributed mining of software repositories (DSK, PN, KB, NM, RFP), pp. 155–159.
MSRMSR-2019-KovalenkoBBB #library #named
PathMiner: a library for mining of path-based representations of code (VK, EB, TB, AB), pp. 13–17.
MSRMSR-2019-MaBAZM #framework #open source
World of code: an infrastructure for mining the universe of open source VCS data (YM, CB, SA, RZ, AM), pp. 143–154.
MSRMSR-2019-MatalongaCC0PSF #android #energy
GreenHub farmer: real-world data for Android energy mining (HM, BC, FC, MC0, RP, SMdS, JPF), pp. 171–175.
SANERSANER-2019-AshrafME #dependence #developer #interactive
Mining Cross-Task Artifact Dependencies from Developer Interactions (UA, CMD, AE), pp. 186–196.
SANERSANER-2019-PachecoBMNMR #framework #recommendation #scala
Mining Scala Framework Extensions for Recommendation Patterns (YP, JDB, TM, DDN, WDM, CDR), pp. 514–523.
SANERSANER-2019-SunBBS #documentation #specification #using
Mining Specifications from Documentation using a Crowd (PS, CB, IB, KTS), pp. 275–286.
SEFMSEFM-2019-Aalst #convergence #process
Object-Centric Process Mining: Dealing with Divergence and Convergence in Event Data (WMPvdA), pp. 3–25.
CoGCoG-2019-YangHZYC0ML #game studies #online #predict
Mining Player In-game Time Spending Regularity for Churn Prediction in Free Online Games (WY, TH, JZ, GY, JC, LC0, SM, YEL), pp. 1–8.
CIKMCIKM-2019-ChelliahZS #multi #overview #recommendation
Recommendation for Multi-stakeholders and through Neural Review Mining (MC, YZ, SS), pp. 2979–2981.
CIKMCIKM-2019-DuanZYZLWWZS0 #learning #multi #summary
Legal Summarization for Multi-role Debate Dialogue via Controversy Focus Mining and Multi-task Learning (XD, YZ, LY, XZ, XL, TW, RW, QZ, CS, FW0), pp. 1361–1370.
CIKMCIKM-2019-KangT #named #network
N2N: Network Derivative Mining (JK, HT), pp. 861–870.
CIKMCIKM-2019-KawabataMS #automation #data type
Automatic Sequential Pattern Mining in Data Streams (KK, YM, YS), pp. 1733–1742.
CIKMCIKM-2019-PaikXL
DTMBIO 2019: The Thirteenth International Workshop on Data and Text Mining in Biomedical Informatics (HP, RX, DL), pp. 2985–2987.
CIKMCIKM-2019-WangCCCHHLC #bound #interactive #mobile #named #process #visualisation
TraVis: An Interactive Visualization System for Mining Inbound Traveler Activities by Leveraging Mobile Ad Request Data (PXW, HC, WQC, CCC, YHH, THH, YL, CHC), pp. 2881–2884.
CIKMCIKM-2019-WangJLHMD #community #network #sentiment #social
Emotional Contagion-Based Social Sentiment Mining in Social Networks by Introducing Network Communities (XW, DJ, ML, DH, KM, JD), pp. 1763–1772.
KDDKDD-2019-Chen0 #data mining #machine learning #optimisation #order #robust
Recent Progress in Zeroth Order Optimization and Its Applications to Adversarial Robustness in Data Mining and Machine Learning (PYC, SL0), pp. 3233–3234.
KDDKDD-2019-Chien #comprehension #learning
Deep Bayesian Mining, Learning and Understanding (JTC), pp. 3197–3198.
KDDKDD-2019-LiuGNWXLLX #comprehension #concept #documentation #query
A User-Centered Concept Mining System for Query and Document Understanding at Tencent (BL, WG, DN, CW, SX, JL, KL, YX), pp. 1831–1841.
KDDKDD-2019-LiX #data mining #named #privacy #scalability
PrivPy: General and Scalable Privacy-Preserving Data Mining (YL, WX), pp. 1299–1307.
KDDKDD-2019-LuSPHM
Investigate Transitions into Drug Addiction through Text Mining of Reddit Data (JL, SS, RP, MAH, GM), pp. 2367–2375.
KDDKDD-2019-PellegrinaRV #named #testing
SPuManTE: Significant Pattern Mining with Unconditional Testing (LP, MR, FV), pp. 1528–1538.
KDDKDD-2019-PellegrinaRV19a #statistics #testing
Hypothesis Testing and Statistically-sound Pattern Mining (LP, MR, FV), pp. 3215–3216.
KDDKDD-2019-RozenshteinG #network
Mining Temporal Networks (PR, AG), pp. 3225–3226.
KDDKDD-2019-ShangSL0 #network
Constructing and Mining Heterogeneous Information Networks from Massive Text (JS, JS, LL, JH0), pp. 3191–3192.
KDDKDD-2019-SpiliopoulouP #comprehension
Mining and Model Understanding on Medical Data (MS, PP), pp. 3223–3224.
KDDKDD-2019-VreekenY #data mining #theory and practice
Modern MDL meets Data Mining Insights, Theory, and Practice (JV, KY), pp. 3229–3230.
KDDKDD-2019-XiaoS #data mining #development #named #tutorial
Tutorial: Data Mining Methods for Drug Discovery and Development (CX, JS), pp. 3195–3196.
KDDKDD-2019-YoshidaTK #graph #learning #metric
Learning Interpretable Metric between Graphs: Convex Formulation and Computation with Graph Mining (TY, IT, MK), pp. 1026–1036.
KDDKDD-2019-ZarrinkalamFB #social
Social User Interest Mining: Methods and Applications (FZ, HF, EB), pp. 3235–3236.
KDDKDD-2019-ZhaCLY #algorithm #roadmap
Mining Algorithm Roadmap in Scientific Publications (HZ, WC, KL, XY), pp. 1083–1092.
KDDKDD-2019-ZhaoGS #keyword #named
Riker: Mining Rich Keyword Representations for Interpretable Product Question Answering (JZ, ZG, HS), pp. 1389–1398.
ASEASE-2019-NamHMMV #api #identification #named #problem #usability
MARBLE: Mining for Boilerplate Code to Identify API Usability Problems (DN, AH, AM, BAM, BV), pp. 615–627.
ESEC-FSEESEC-FSE-2019-OlssonF #ecosystem #industrial #risk management
Risks and assets: a qualitative study of a software ecosystem in the mining industry (TO, UF), pp. 895–904.
ICSE-2019-0008ZBPL
Pattern-based mining of opinions in Q&A websites (BL0, FZ, GB, MDP, ML), pp. 548–559.
ICSE-2019-AmarR #debugging #fault #locality #predict
Mining historical test logs to predict bugs and localize faults in the test logs (AA, PCR), pp. 140–151.
ICSE-2019-DmeiriTWBLDVR #dataset #named
BugSwarm: mining and continuously growing a dataset of reproducible failures and fixes (DAT, ND, YW, AB, YCL, PTD, BV, CRG), pp. 339–349.
ICSE-2019-NguyenNDNTH #fine-grained #graph #semantics
Graph-based mining of in-the-wild, fine-grained, semantic code change patterns (HAN, TNN, DD, SN, HT, MH), pp. 819–830.
ICSE-2019-NguyenRRODP #api #named #recommendation
FOCUS: a recommender system for mining API function calls and usage patterns (PTN, JDR, DDR, LO, TD, MDP), pp. 1050–1060.
ICSE-2019-YatishJTT #fault #question
Mining software defects: should we consider affected releases? (SY, JJ, PT, CT), pp. 654–665.
CASECASE-2019-Dagnino #data mining #process
Data Mining Methods to Analyze Alarm Logs in IoT Process Control Systems (AD), pp. 323–330.
CASECASE-2019-TheisMD #logic #process #programmable
Process Mining of Programmable Logic Controllers: Input/Output Event Logs (JT, IM, HD), pp. 216–221.
EDMEDM-2018-AlsaadBGSZ #concept #dependence #graph
Mining MOOC Lecture Transcripts to Construct Concept Dependency Graphs (FA, AB, CG, HS, CZ).
EDMEDM-2018-BoubekkiJB
Mining User Trajectories in Electronic Text Books (AB, SJ, UB).
EDMEDM-2018-ChenLG #data mining #education #online
Re-designing the Structure of Online Courses to Empower Educational Data Mining (ZC, SL, GG).
EDMEDM-2018-ChopraG #learning
Job Description Mining to Understand Work-Integrated Learning (SC, LG).
EDMEDM-2018-ChopraGKMG #approach #difference #gender
Gender Differences in Undergraduate Engineering Applicants: A Text Mining Approach (SC, HG, AK, MM, LG).
EDMEDM-2018-Perez-LemoncheD #concept #student
Mining Student Misconceptions from Pre- and Post-Testing Data (ÁPL, BD, DEP).
ICPCICPC-2018-Ko #challenge #comprehension
Mining the mind, minding the mine: grand challenges in comprehension and mining (AJK0), p. 1.
ICSMEICSME-2018-LiLSXPLZ #api #graph
Improving API Caveats Accessibility by Mining API Caveats Knowledge Graph (HL, SL, JS, ZX, XP0, ML, XZ), pp. 183–193.
ICSMEICSME-2018-SaidQK #embedded #on the #state machine
On State Machine Mining from Embedded Control Software (WS, JQ, RK), pp. 138–148.
MSRMSR-2018-JoonbakhshS #ide #interactive #metric #process
Mining and extraction of personal software process measures through IDE interaction logs (AJ, AS), pp. 78–81.
MSRMSR-2018-Ko #challenge #comprehension
Mining the mind, minding the mine: grand challenges in comprehension and mining (AJK0), p. 118.
MSRMSR-2018-MajumderBBFM08 #case study #learning #performance #stack overflow
500+ times faster than deep learning: a case study exploring faster methods for text mining stackoverflow (SM, NB, KB, WF0, TM), pp. 554–563.
MSRMSR-2018-Russo #profiling
Profiling call changes via motif mining (BR), pp. 203–214.
SANERSANER-2018-BaoLL #question
Mining sandboxes: Are we there yet? (LB, TDBL, DL0), pp. 445–455.
SANERSANER-2018-ChatleyJ #automation #code review #named #overview #repository
Diggit: Automated code review via software repository mining (RC, LJ), pp. 567–571.
SANERSANER-2018-HossainVHKJS #api
Mining accurate message formats for service APIs (MAH, SV, JH0, MAK, JJ, JGS), pp. 266–276.
SANERSANER-2018-LeemansAB18a #analysis #process #scalability #statechart #using
The Statechart Workbench: Enabling scalable software event log analysis using process mining (ML, WMPvdA, MGJvdB), pp. 502–506.
SANERSANER-2018-LiuZ #program repair #stack overflow
Mining stackoverflow for program repair (XL, HZ), pp. 118–129.
SANERSANER-2018-MoverSOC #corpus #framework #graph
Mining framework usage graphs from app corpora (SM, SS0, RBPO, BYEC), pp. 277–289.
SANERSANER-2018-Salem #agile #identification #named
GoldRusher: A miner for rapid identification of hidden code (AS), pp. 517–521.
AIIDEAIIDE-2018-YoonAHK #architecture #design
Design Mining for Minecraft Architecture (EY, EA, BH, RAK), pp. 250–256.
CIKMCIKM-2018-AslayNMG #evolution #graph
Mining Frequent Patterns in Evolving Graphs (ÇA, MAUN, GDFM, AG), pp. 923–932.
CIKMCIKM-2018-GalimbertiBBCG #network
Mining (maximal) Span-cores from Temporal Networks (EG, AB, FB, CC, FG), pp. 107–116.
CIKMCIKM-2018-KweeCPL #lifecycle #named #using #visualisation
Traffic-Cascade: Mining and Visualizing Lifecycles of Traffic Congestion Events Using Public Bus Trajectories (ATK, MFC, PKP, EPL), pp. 1955–1958.
CIKMCIKM-2018-LeiXWP #behaviour #named
CAPatternMiner: Mining Ship Collision Avoidance Behavior from AIS Trajectory Data (PRL, LPX, YTW, WCP), pp. 1875–1878.
CIKMCIKM-2018-MundraKSM #comprehension
Mining & Summarizing E-petitions for Enhanced Understanding of Public Opinion (SM, SK, MS, SM), pp. 1695–1698.
CIKMCIKM-2018-VazirgiannisMN #graph #information retrieval #named
GraphRep: Boosting Text Mining, NLP and Information Retrieval with Graphs (MV, FDM, GN), pp. 2295–2296.
CIKMCIKM-2018-WangYWJZZW #data mining #graph #named #scalability
AceKG: A Large-scale Knowledge Graph for Academic Data Mining (RW, YY, JW, YJ, YZ, WZ0, XW), pp. 1487–1490.
ECIRECIR-2018-GharebaghRN #agile #approach #novel
T-Shaped Mining: A Novel Approach to Talent Finding for Agile Software Teams (SSG, PR, MN), pp. 411–423.
ECIRECIR-2018-TrikhaZB #detection #topic
Topic-Association Mining for User Interest Detection (AKT, FZ, EB), pp. 665–671.
ICPRICPR-2018-BicegoLDGA #modelling #topic #using
Mining NMR Spectroscopy Using Topic Models (MB, PL, MD, FG, MA), pp. 3784–3789.
ICPRICPR-2018-LiuZWWZ #identification #multi
Identification of Hypertension by Mining Class Association Rules from Multi-dimensional Features (FL0, XZ, ZW, TW, YZ), pp. 3114–3119.
ICPRICPR-2018-QieG00W #correlation
Improved Correlation Filter Tracking with Hard Negative Mining (CQ, GG, YY0, LZ0, HW), pp. 1646–1651.
KDDKDD-2018-DuT #equation #graph #named #performance
FASTEN: Fast Sylvester Equation Solver for Graph Mining (BD, HT), pp. 1339–1347.
KDDKDD-2018-PeakeW #modelling #recommendation
Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems (GP, JW), pp. 2060–2069.
KDDKDD-2018-PellegrinaV #mutation testing #performance #permutation #testing
Efficient Mining of the Most Significant Patterns with Permutation Testing (LP, FV), pp. 2070–2079.
KDDKDD-2018-RiondatoV #named #pseudo
MiSoSouP: Mining Interesting Subgroups with Sampling and Pseudodimension (MR, FV), pp. 2130–2139.
PLATEAUPLATEAU-2018-Lemay #comprehension #git #java #repository #usability
Understanding Java Usability by Mining GitHub Repositories (MJL), p. 9.
ASEASE-2018-KovalenkoPB #branch #question
Mining file histories: should we consider branches? (VK, FP, AB), pp. 202–213.
ASEASE-2018-Krismayer #automation #constraints #monitoring
Automatic mining of constraints for monitoring systems of systems (TK), pp. 924–927.
ESEC-FSEESEC-FSE-2018-DeFreez #specification
Mining error-handling specifications for systems software (DD), pp. 983–985.
ESEC-FSEESEC-FSE-2018-DeFreezTR #specification
Path-based function embedding and its application to error-handling specification mining (DD, AVT, CRG), pp. 423–433.
ESEC-FSEESEC-FSE-2018-LeB0 #named #network #specification #using
DSM: a specification mining tool using recurrent neural network based language model (TDBL, LB, DL0), pp. 896–899.
ESEC-FSEESEC-FSE-2018-SpadiniAB #framework #named #python #repository
PyDriller: Python framework for mining software repositories (DS, MFA, AB), pp. 908–911.
ICSE-2018-YuJ #data flow #dependence
Dataflow tunneling: mining inter-request data dependencies for request-based applications (XY, GJ), pp. 586–597.
ICSTICST-2018-PradhanW00L #multi #named #testing #using
REMAP: Using Rule Mining and Multi-objective Search for Dynamic Test Case Prioritization (DP, SW0, SA0, TY0, ML), pp. 46–57.
JCDLJCDL-2017-SaggionR #data mining
Scholarly Data Mining: Making Sense of Scientific Literature (HS, FR), pp. 346–347.
EDMEDM-2017-BeckCB #data mining #education #learning
Workshop proposal: deep learning for educational data mining (JB, MC, RSB).
EDMEDM-2017-LynchBXG #data mining #education #graph
Graph-based Educational Data Mining (CL, TB, LX, NG).
EDMEDM-2017-MadaioLCO #predict #using
Using Temporal Association Rule Mining to Predict Dyadic Rapport in Peer Tutoring (MAM, RL, JC, AO).
EDMEDM-2017-ThanasuanCW #learning #student
Emerging Patterns in Student's Learning Attributes through Text Mining (KT, WC, CW).
EDMEDM-2017-XueLC #diagrams #graph grammar
Mining Innovative Augmented Graph Grammars for Argument Diagrams through Novelty Selection (LX, CL, MC).
ICPCICPC-2017-NguyenJQ #android #detection #repository
Android repository mining for detecting publicly accessible functions missing permission checks (HHN, LJ, TTQ), pp. 324–327.
ICSMEICSME-2017-Gupta #maintenance #predict #process #using
Improving Software Maintenance Using Process Mining and Predictive Analytics (MG0), pp. 681–686.
ICSMEICSME-2017-Li
Mining AndroZoo: A Retrospect (LL), pp. 675–680.
MSRMSR-2017-BaoXXLL #developer #industrial #scalability
Who will leave the company?: a large-scale industry study of developer turnover by mining monthly work report (LB, ZX, XX0, DL0, SL), pp. 170–181.
MSRMSR-2017-CorbelliniGMZL #repository #social #web #web service
Mining social web service repositories for social relationships to aid service discovery (AC, DG, CM, AZ, IL), pp. 75–79.
MSRMSR-2017-MolderezSR
Mining change histories for unknown systematic edits (TM, RS, CDR), pp. 248–256.
MSRMSR-2017-TiwariUNR #framework #named #platform #repository #tool support
Candoia: a platform for building and sharing mining software repositories tools as apps (NMT, GU, HAN, HR), pp. 53–63.
SANERSANER-2017-TangL #approach #named #novel #probability #using
StiCProb: A novel feature mining approach using conditional probability (YT, HL), pp. 45–55.
CIKMCIKM-2017-CaoZL #approach #approximate #distributed #effectiveness #graph #named #scalability
PMS: an Effective Approximation Approach for Distributed Large-scale Graph Data Processing and Mining (YC, YZ, JL), pp. 1999–2002.
CIKMCIKM-2017-ChavaryEL #network #using
Summarizing Significant Changes in Network Traffic Using Contrast Pattern Mining (EAC, SME, CL), pp. 2015–2018.
CIKMCIKM-2017-HoangL #clustering #network #performance
Highly Efficient Mining of Overlapping Clusters in Signed Weighted Networks (TAH, EPL), pp. 869–878.
CIKMCIKM-2017-HuJ #data mining #modelling
IDM 2017: Workshop on Interpretable Data Mining - Bridging the Gap between Shallow and Deep Models (XH, SJ), pp. 2565–2566.
KDDKDD-2017-Berglund #big data
Mining Big Data in NeuroGenetics to Understand Muscular Dystrophy (AB), p. 11.
KDDKDD-2017-BifetZFHZQHP #data type #evolution #performance
Extremely Fast Decision Tree Mining for Evolving Data Streams (AB, JZ, WF0, CH, JZ, JQ, GH0, BP), pp. 1733–1742.
KDDKDD-2017-BlalockG #data mining #named #performance
Bolt: Accelerated Data Mining with Fast Vector Compression (DWB, JVG), pp. 727–735.
KDDKDD-2017-GanH #data mining #framework #scalability
A Data Mining Framework for Valuing Large Portfolios of Variable Annuities (GG, JXH), pp. 1467–1475.
KDDKDD-2017-HopeCKS
Accelerating Innovation Through Analogy Mining (TH, JC, AK, DS), pp. 235–243.
KDDKDD-2017-KomiyamaIANM #multi #statistics #testing
Statistical Emerging Pattern Mining with Multiple Testing Correction (JK, MI, HA, TN, SiM), pp. 897–906.
KDDKDD-2017-LabutovHBH #learning
Semi-Supervised Techniques for Mining Learning Outcomes and Prerequisites (IL, YH0, PB, DH), pp. 907–915.
KDDKDD-2017-WangHCLYR #network
Structural Deep Brain Network Mining (SW, LH0, BC, CTL, PSY, ABR), pp. 475–484.
OOPSLAOOPSLA-2017-SamanthaNNR #problem #specification
Exploiting implicit beliefs to resolve sparse usage problem in usage-based specification mining (SKS, HAN, TNN, HR), p. 29.
ASEASE-2017-Han #question #re-engineering
Mining structures from massive text data: will it help software engineering? (JH0), p. 2.
ASEASE-2017-KrismayerRG #constraints #monitoring
Mining constraints for event-based monitoring in systems of systems (TK, RR, PG), pp. 826–831.
ASEASE-2017-LinMXXSPLZD #design #reuse
Mining implicit design templates for actionable code reuse (YL0, GM, YX, ZX, JS0, XP0, YL0, WZ, JSD), pp. 394–404.
ASEASE-2017-SchmidtNF #named #specification
TREM: a tool for mining timed regular specifications from system traces (LS, AN, SF), pp. 901–906.
ASEASE-2017-ToffolaSP #effectiveness #exclamation #generative #testing
Saying 'hi!' is not enough: mining inputs for effective test generation (LDT, CAS, MP), pp. 44–49.
ESEC-FSEESEC-FSE-2017-HuijgensLSRGR #agile #delivery #metric #power of #predict
Strong agile metrics: mining log data to determine predictive power of software metrics for continuous delivery teams (HH, RL, DS, HR, GG, DR), pp. 866–871.
CASECASE-2017-LuLXZB #data mining #industrial #process #research
Research on data mining service and its application case in complex industrial process (QL, ZJL, QX, YZ, JB), pp. 1124–1129.
ICSTICST-2017-WanLXCL #linux
Mining Sandboxes for Linux Containers (ZW, DL0, XX0, LC, SL), pp. 92–102.
WICSAWICSA-2016-YuanM #architecture #component #detection #interactive #security
Mining Software Component Interactions to Detect Security Threats at the Architectural Level (EY, SM), pp. 211–220.
JCDLJCDL-2016-BudigDK
Glyph Miner: A System for Efficiently Extracting Glyphs from Early Prints in the Context of OCR (BB, TCvD, FK), pp. 31–34.
JCDLJCDL-2016-ClarkD #research
PDFFigures 2.0: Mining Figures from Research Papers (CAC, SKD), pp. 143–152.
EDMEDM-2016-BuffumFBWML #assessment #collaboration #embedded #learning #sequence
Mining Sequences of Gameplay for Embedded Assessment in Collaborative Learning (PSB, MF, KEB, ENW, BWM, JCL), pp. 575–576.
EDMEDM-2016-DongKB #comparison #learning #multi #process
Comparison of Selection Criteria for Multi-Feature Hierarchical Activity Mining in Open Ended Learning Environments (YD, JSK, GB), pp. 591–592.
EDMEDM-2016-HaoLDKK #analysis #collaboration #data mining #problem #statistics
Collaborative Problem Solving Skills versus Collaboration Outcomes: Findings from Statistical Analysis and Data Mining (JH, LL, AvD, PCK, CK), pp. 382–387.
EDMEDM-2016-JiangG #approach #contest #graph #on the
On Competition for Undergraduate Co-op Placements: A Graph Mining Approach (YHJ, LG), pp. 394–399.
EDMEDM-2016-McBroomJKY #behaviour #student
Mining behaviors of students in autograding submission system logs (JM, BJ, IK, KY), pp. 159–166.
EDMEDM-2016-Penteado #assessment #data mining #estimation #scalability #semantics #using
Estimation of prerequisite skills model from large scale assessment data using semantic data mining (BEP), pp. 675–677.
EDMEDM-2016-Rau #concept #learning #physics #social
Pattern mining uncovers social prompts of conceptual learning with physical and virtual representations (MAR), pp. 478–483.
EDMEDM-2016-SabourinMW #data mining #education #tool support
SAS Tools for Educational Data Mining (JS, SWM, ADW), pp. 632–633.
EDMEDM-2016-Sherzad #challenge #data mining #education
Applicability of Educational Data Mining in Afghanistan: Opportunities and Challenges (ARS), pp. 634–635.
EDMEDM-2016-ShimadaOO #matrix
Browsing-Pattern Mining from e-Book Logs with Non-negative Matrix Factorization (AS, FO, HO), pp. 636–637.
ICPCICPC-2016-SaiedS #api #approach
A cooperative approach for combining client-based and library-based API usage pattern mining (MAS, HAS), pp. 1–10.
MSRMSR-2016-AhasanuzzamanAR #stack overflow
Mining duplicate questions in stack overflow (MA, MA, CKR, KAS), pp. 402–412.
MSRMSR-2016-AveryDSG #behaviour
Externalization of software behavior by the mining of norms (DA, HKD, BTRS, AKG), pp. 223–234.
MSRMSR-2016-BaoLXWT #android #commit #developer #empirical #how #power management
How android app developers manage power consumption?: an empirical study by mining power management commits (LB, DL0, XX0, XW0, CT), pp. 37–48.
MSRMSR-2016-GomezRAS #android #automation #detection #performance #repository #user interface
Mining test repositories for automatic detection of UI performance regressions in Android apps (MG0, RR, BA, LS), pp. 13–24.
MSRMSR-2016-Hassan #case study #education #experience #repository #research
Raising MSR researchers: an experience report on teaching a graduate seminar course in mining software repositories (MSR) (AEH), pp. 121–125.
MSRMSR-2016-LuoPG #evolution #performance
Mining performance regression inducing code changes in evolving software (QL, DP, MG), pp. 25–36.
MSRMSR-2016-MantylaADGO #detection #question
Mining valence, arousal, and dominance: possibilities for detecting burnout and productivity? (MM, BA, GD, DG, MO), pp. 247–258.
MSRMSR-2016-MoslehiAR #documentation #on the #speech
On mining crowd-based speech documentation (PM, BA, JR), pp. 259–268.
MSRMSR-2016-TrautschHMG #framework #platform #problem #repository
Adressing problems with external validity of repository mining studies through a smart data platform (FT, SH, PM, JG), pp. 97–108.
MSRMSR-2016-YangKYI #code review #dataset #overview #people #process #repository
Mining the modern code review repositories: a dataset of people, process and product (XY, RGK, NY, HI), pp. 460–463.
SANERSANER-2016-ChenGX #category theory #library #relational #word
Mining Analogical Libraries in Q&A Discussions - Incorporating Relational and Categorical Knowledge into Word Embedding (CC, SG, ZX), pp. 338–348.
SANERSANER-2016-KarimKP #android #recommendation
Mining Android Apps to Recommend Permissions (MYK, HHK, MDP), pp. 427–437.
SANERSANER-2016-WilleSSS #variability
Custom-Tailored Variability Mining for Block-Based Languages (DW, SS, CS0, IS), pp. 271–282.
SCAMSCAM-2016-MoonenARB #impact analysis
Exploring the Effects of History Length and Age on Mining Software Change Impact (LM, SDA, TR, DWB), pp. 207–216.
CHI-PLAYCHI-PLAY-2016-WellsCLMGS #data mining #network
Mining for Gold (and Platinum): PlayStation Network Data Mining (LW, AJCS, IJL, LM, BG, KdS), pp. 304–312.
CoGCIG-2016-SephtonCDHS #android #predict #using
Using association rule mining to predict opponent deck content in android: Netrunner (NS, PIC, SD, VJH, NHS), pp. 1–8.
CIKMCIKM-2016-AgrawalC #data mining #predict #using
A Fatigue Strength Predictor for Steels Using Ensemble Data Mining: Steel Fatigue Strength Predictor (AA, ANC), pp. 2497–2500.
CIKMCIKM-2016-ChenDWST #data mining #recommendation
From Recommendation to Profile Inference (Rec2PI): A Value-added Service to Wi-Fi Data Mining (CC0, FD, KW0, VS0, AT), pp. 1503–1512.
CIKMCIKM-2016-HuSWXL
Mining Shopping Patterns for Divergent Urban Regions by Incorporating Mobility Data (TH, RS, YW, XX0, JL), pp. 569–578.
CIKMCIKM-2016-KimCCL
DTMBIO 2016: The Tenth International Workshop on Data and Text Mining in Biomedical Informatics (SK, JYC, VC, DL), pp. 2511–2512.
CIKMCIKM-2016-LuCL #recommendation #topic #video
Scarce Feature Topic Mining for Video Recommendation (WL0, KFLC, KL), pp. 1993–1996.
CIKMCIKM-2016-NadungodageXL #data type #framework #named
GStreamMiner: A GPU-accelerated Data Stream Mining Framework (CHN, YX, JJL), pp. 2489–2492.
CIKMCIKM-2016-ParkJLK #named
BIGtensor: Mining Billion-Scale Tensor Made Easy (NP, BJ, JL, UK), pp. 2457–2460.
CIKMCIKM-2016-SantuSZ #generative #modelling
Generative Feature Language Models for Mining Implicit Features from Customer Reviews (SKKS, PS, CZ), pp. 929–938.
CIKMCIKM-2016-ShiTWA #big data #data mining #visual notation
ACM DAVA'16: 2nd International Workshop on DAta mining meets Visual Analytics at Big Data Era (LS, HT, CW, LA), p. 2509.
CIKMCIKM-2016-WangH #database #incremental
Incremental Mining of High Utility Sequential Patterns in Incremental Databases (JZW, JLH), pp. 2341–2346.
ECIRECIR-2016-IencoRRRT #learning #modelling #multi
MultiLingMine 2016: Modeling, Learning and Mining for Cross/Multilinguality (DI, MR, SR, PR, AT), pp. 869–873.
ECIRECIR-2016-KimSL #topic
Subtopic Mining Based on Three-Level Hierarchical Search Intentions (SJK, JS, JHL), pp. 741–747.
ICPRICPR-2016-AydinA #evolution #sequence
Spatiotemporal event sequence mining from evolving regions (BA, RAA), pp. 4172–4177.
ICPRICPR-2016-Liu #hybrid #scalability
Exposing seam carving forgery under recompression attacks by hybrid large feature mining (QL), pp. 1041–1046.
KDDKDD-2016-BanerjeeYR #approach #named #scalability
MANTRA: A Scalable Approach to Mining Temporally Anomalous Sub-trajectories (PB, PY, SR), pp. 1415–1424.
KDDKDD-2016-FowkesS #sequence
A Subsequence Interleaving Model for Sequential Pattern Mining (JMF, CAS), pp. 835–844.
KDDKDD-2016-GaoLZFH #crowdsourcing #reliability
Mining Reliable Information from Passively and Actively Crowdsourced Data (JG0, QL0, BZ0, WF0, JH0), pp. 2121–2122.
KDDKDD-2016-HajianBC #algorithm #bias #data mining
Algorithmic Bias: From Discrimination Discovery to Fairness-aware Data Mining (SH, FB, CC0), pp. 2125–2126.
KDDKDD-2016-Lemmerich0SHHS #behaviour
Mining Subgroups with Exceptional Transition Behavior (FL, MB0, PS, DH, AH, MS), pp. 965–974.
KDDKDD-2016-MoralesBKGF #big data #data type
IoT Big Data Stream Mining (GDFM, AB, LK, JG, WF0), pp. 2119–2120.
KDDKDD-2016-NakagawaSKTT #approach #performance #predict
Safe Pattern Pruning: An Efficient Approach for Predictive Pattern Mining (KN, SS, MK, KT, IT), pp. 1785–1794.
KDDKDD-2016-NandiMADB #control flow #detection #execution #graph #using
Anomaly Detection Using Program Control Flow Graph Mining From Execution Logs (AN, AM, SA, GBD, SB), pp. 215–224.
KDDKDD-2016-NguyenP
Text Mining in Clinical Domain: Dealing with Noise (HN, JP), pp. 549–558.
KDDKDD-2016-PrakashR #algorithm #data mining #modelling
Leveraging Propagation for Data Mining: Models, Algorithms and Applications (BAP, NR), pp. 2133–2134.
KDDKDD-2016-WangZD #data mining #matrix #modelling
Healthcare Data Mining with Matrix Models (FW0, PZ0, JD), pp. 2137–2138.
KDDKDD-2016-YangYWCZL
Diversified Temporal Subgraph Pattern Mining (YY, DY0, HW, JC, SZ, JCSL), pp. 1965–1974.
SKYSKY-2016-Speicher16a #source code
Mining Source Code Regularities with FCA and Association Rules (DS), pp. 75–77.
MoDELSMoDELS-2016-HebigHCRF #git #open source #uml
The quest for open source projects that use UML: mining GitHub (RH, THQ, MRVC, GR, MAF), pp. 173–183.
MoDELSMoDELS-2016-KalenkovaALR #modelling #process #using
Process mining using BPMN: relating event logs and process models (AAK, WMPvdA, IAL, VAR), p. 123.
ASEASE-2016-ChengPJZYZ #detection
Mining revision histories to detect cross-language clones without intermediates (XC, ZP, LJ, HZ, HY, JZ), pp. 696–701.
ASEASE-2016-Greene0 #developer #identification #named #open source
CVExplorer: identifying candidate developers by mining and exploring their open source contributions (GJG, BF), pp. 804–809.
ASEASE-2016-HoscheleZ
Mining input grammars from dynamic taints (MH, AZ), pp. 720–725.
ASEASE-2016-MoonenABR #guidelines #recommendation #using
Practical guidelines for change recommendation using association rule mining (LM, SDA, DB, TR), pp. 732–743.
FSEFSE-2016-BruninkR #performance #specification
Mining performance specifications (MB, DSR), pp. 39–49.
FSEFSE-2016-FowkesS #api #git #probability
Parameter-free probabilistic API mining across GitHub (JMF, CAS), pp. 254–265.
ICSE-2016-JamrozikSZ
Mining sandboxes (KJ, PvSR, AZ), pp. 37–48.
ICSE-2016-LiangBZSYC #debugging #named
AntMiner: mining more bugs by reducing noise interference (BL0, PB, YZ, WS, WY, YC0), pp. 333–344.
ICSE-2016-LuLLXMH0F #android #named #scalability
PRADA: prioritizing android devices for apps by mining large-scale usage data (XL, XL, HL, TX0, QM, DH, GH0, FF0), pp. 3–13.
CASECASE-2016-ChengZWCJL #case study #data mining #energy #fault #using
Case studies of fault diagnosis and energy saving in buildings using data mining techniques (ZC, QZ, FW, ZC, YJ, YL), pp. 646–651.
HTHT-2015-Orellana-Rodriguez #recommendation
Mining Affective Context in Short Films for Emotion-Aware Recommendation (COR, EDA, WN), pp. 185–194.
SIGMODSIGMOD-2015-AmsterdamerKM #interface #named #natural language
NL2CM: A Natural Language Interface to Crowd Mining (YA, AK, TM), pp. 1433–1438.
SIGMODSIGMOD-2015-BeedkarG #named #scalability #sequence
LASH: Large-Scale Sequence Mining with Hierarchies (KB, RG), pp. 491–503.
SIGMODSIGMOD-2015-GurukarRR #approach #commit #communication #named #network #scalability
COMMIT: A Scalable Approach to Mining Communication Motifs from Dynamic Networks (SG, SR, BR), pp. 475–489.
SIGMODSIGMOD-2015-LiuSWRH #corpus #quality
Mining Quality Phrases from Massive Text Corpora (JL, JS, CW, XR, JH), pp. 1729–1744.
SIGMODSIGMOD-2015-SakuraiMF
Mining and Forecasting of Big Time-series Data (YS, YM, CF), pp. 919–922.
SIGMODSIGMOD-2015-TrummerHLSG #web
Mining Subjective Properties on the Web (IT, AYH, HL, SS, RG), pp. 1745–1760.
VLDBVLDB-2015-DoLW
Mining Revenue-Maximizing Bundling Configuration (LD, HWL, KW), pp. 593–604.
VLDBVLDB-2015-KoutraJNF #graph #interactive #named #scalability #visualisation
Perseus: An Interactive Large-Scale Graph Mining and Visualization Tool (DK, DJ, YN, CF), pp. 1924–1935.
EDMEDM-2015-AgnihotriAMRE #student
Mining Login Data For Actionable Student Insight (LA, AA, SM, MR, AE), pp. 472–475.
EDMEDM-2015-BravoRLP #data mining #education #online #tool support
Exploring the Influence of ICT in online Education Through Data Mining Tools (JB, SJR, JML, SP), pp. 540–543.
EDMEDM-2015-ChenWL #probability
Discovering Prerequisite Structure of Skills through Probabilistic Association Rules Mining (YC0, PHW, JML), pp. 117–124.
EDMEDM-2015-CrossleyMBWPBB #data mining #education #online
Language to Completion: Success in an Educational Data Mining Massive Open Online Class (SAC, DSM, RSB, YW, LP, TB, YB), pp. 388–391.
EDMEDM-2015-CuadrosGRGZO #data mining #education
Educational Data Mining in an Open-Ended Remote Laboratory on Electric Circuits. Goals and Preliminary Results (JC, LGS, SR, MLG, JGZ, PO), pp. 578–579.
EDMEDM-2015-Olivares-Rodriguez #learning #student #word
Learning the Creative Potential of Students by Mining a Word Association Task (COR, MG), pp. 400–403.
EDMEDM-2015-RiddleBGMCCP #identification
Mining a Written Values Affirmation Intervention to Identify the Unique Linguistic Features of Stigmatized Groups (TR, SB, WG, SM, GC, JC, VPV), pp. 274–281.
EDMEDM-2015-SabourinKFM #data mining #education #industrial #privacy #student
Student Privacy and Educational Data Mining: Perspectives from Industry (JS, LK, CF, SWM), pp. 164–170.
EDMEDM-2015-Tibbles #data mining #learning
Exploring the Impact of Spacing in Mathematics Learning through Data Mining (RT), pp. 590–591.
ICSMEICSME-2015-NagyC #fault #query #sql #stack overflow
Mining Stack Overflow for discovering error patterns in SQL queries (CN, AC), pp. 516–520.
ICSMEICSME-2015-ValeM #architecture #dynamic analysis #named #using
Keecle: Mining key architecturally relevant classes using dynamic analysis (LdNV, MdAM), pp. 566–570.
ICSMEICSME-2015-WangPV #corpus #scalability
Developing a model of loop actions by mining loop characteristics from a large code corpus (XW, LLP, KVS), pp. 51–60.
MSRMSR-2015-AbateCGFTZ #component #repository
Mining Component Repositories for Installability Issues (PA, RDC, LG, FLF, RT, SZ), pp. 24–33.
MSRMSR-2015-CalefatoLMN #stack overflow
Mining Successful Answers in Stack Overflow (FC, FL, MCM, NN), pp. 430–433.
MSRMSR-2015-ChowdhuryH #stack overflow #topic
Mining StackOverflow to Filter Out Off-Topic IRC Discussion (SAC, AH), pp. 422–425.
MSRMSR-2015-Marinescu
Confessions of a Worldly Software Miner (RM), p. 1.
MSRMSR-2015-MartinHJSZ #problem
The App Sampling Problem for App Store Mining (WM, MH, YJ, FS, YZ), pp. 123–133.
MSRMSR-2015-MouraPEC #commit #energy
Mining Energy-Aware Commits (IM, GP, FE, FC), pp. 56–67.
MSRMSR-2015-PonzanelliML #development #semistructured data
Summarizing Complex Development Artifacts by Mining Heterogeneous Data (LP, AM, ML), pp. 401–405.
MSRMSR-2015-VasquezWBMP #android #execution #generative
Mining Android App Usages for Generating Actionable GUI-Based Execution Scenarios (MLV, MW, CBC, KM, DP), pp. 111–122.
SANERSANER-2015-LeL #metric #rule-based #specification
Beyond support and confidence: Exploring interestingness measures for rule-based specification mining (TDBL, DL), pp. 331–340.
SANERSANER-2015-MondalRS #named #refactoring
SPCP-Miner: A tool for mining code clones that are important for refactoring or tracking (MM, CKR, KAS), pp. 484–488.
SANERSANER-2015-SaiedBAS #api #multi
Mining Multi-level API Usage Patterns (MAS, OB, HA, HAS), pp. 23–32.
SANERSANER-2015-WongLT #automation #generative #named #source code
CloCom: Mining existing source code for automatic comment generation (EW, TL, LT), pp. 380–389.
SCAMSCAM-2015-PanditaJSW #api #using
Discovering likely mappings between APIs using text mining (RP, RPJ, SDS, LAW), pp. 231–240.
LATALATA-2015-ChoHK
Frequent Pattern Mining with Non-overlapping Inversions (DJC, YSH, HK), pp. 121–132.
CoGCIG-2015-DobreL #game studies #learning #online
Online learning and mining human play in complex games (MSD, AL), pp. 60–67.
CoGCIG-2015-WehrD #game studies
Mining game logs to create a playbook for unit AIs (DW, JD), pp. 391–398.
CHICHI-2015-CaoLLT #graph #interactive #multi #named #visual notation
g-Miner: Interactive Visual Group Mining on Multivariate Graphs (NC, YRL, LL, HT), pp. 279–288.
CHICHI-2015-SadauskasBA #design #framework #platform #social #social media
Mining Memories: Designing a Platform to Support Social Media Based Writing (JS, DB, RKA), pp. 3691–3700.
CAiSECAiSE-2015-EckLLA #named #process
PM²: A Process Mining Project Methodology (MLvE, XL, SJJL, WMPvdA), pp. 297–313.
ICEISICEIS-v1-2015-Aghbari #big data #challenge
Mining Big Data — Challenges and Opportunities (ZAA), pp. 379–384.
ICEISICEIS-v1-2015-Castanon-PugaSG #algorithm #data mining #fuzzy #logic #mobile #using
Hybrid-Intelligent Mobile Indoor Location Using Wi-Fi Signals — Location Method Using Data Mining Algorithms and Type-2 Fuzzy Logic Systems (MCP, ASC, CGP, GLS, MFP, EAT), pp. 609–615.
ICEISICEIS-v1-2015-SunB #classification #process
Function-based Case Classification for Improving Business Process Mining (YS, BB), pp. 251–258.
ICEISICEIS-v2-2015-AngioniCT #collaboration
Evaluating Potential Improvements of Collaborative Filtering with Opinion Mining (MA, MLC, FT), pp. 656–661.
ICEISICEIS-v2-2015-FariasNOCM #case study #commit #email #repository
Analyzing Distributions of Emails and Commits from OSS Contributors through Mining Software Repositories — An Exploratory Study (MAdFF, RLN, PO, MCJ, MGM), pp. 303–310.
ICEISICEIS-v2-2015-TangL #framework #product line #top-down
Top-down Feature Mining Framework for Software Product Line (YT, HL), pp. 71–81.
ICEISICEIS-v3-2015-ThabetGG #analysis #petri net #process
Petri Net Model Cost Extension based on Process Mining — Cost Data Description and Analysis (DT, SAG, HHBG), pp. 268–275.
CIKMCIKM-2015-DuanZ #coordination #recommendation #representation
Mining Coordinated Intent Representation for Entity Search and Recommendation (HD, CZ), pp. 333–342.
CIKMCIKM-2015-ParkKPCNYR #chat #online
Mining the Minds of Customers from Online Chat Logs (KP, JK, JP, MC, JN, SY0, ER), pp. 1879–1882.
CIKMCIKM-2015-PengZLHWZ #microblog #topic
Central Topic Model for Event-oriented Topics Mining in Microblog Stream (MP, JZ, XL, JH, HW0, YZ), pp. 1611–1620.
CIKMCIKM-2015-SongHL #network #social
Mining Brokers in Dynamic Social Networks (CS, WH, MLL), pp. 523–532.
CIKMCIKM-2015-SongLV
DTMBIO 2015: International Workshop on Data and Text Mining in Biomedical Informatics (MS0, DL, KV), pp. 1941–1942.
CIKMCIKM-2015-TandonMDW #named #process
Knowlywood: Mining Activity Knowledge From Hollywood Narratives (NT, GdM, AD, GW), pp. 223–232.
CIKMCIKM-2015-WangHCWL #aspect-oriented #microblog #named #sentiment
ASEM: Mining Aspects and Sentiment of Events from Microblog (RW, WH, WC0, TW, KL), pp. 1923–1926.
ECIRECIR-2015-Dutta #approximate #named #statistics #string #using
MIST: Top-k Approximate Sub-string Mining Using Triplet Statistical Significance (SD), pp. 284–290.
ECIRECIR-2015-Moghaddam #analysis #fault #feedback #sentiment
Beyond Sentiment Analysis: Mining Defects and Improvements from Customer Feedback (SM), pp. 400–410.
KDDKDD-2015-ArlorioCLLP #assessment #authentication #data mining
Exploiting Data Mining for Authenticity Assessment and Protection of High-Quality Italian Wines from Piedmont (MA, JDC, GL, ML, LP), pp. 1671–1680.
KDDKDD-2015-CaiTFJH #higher-order #named #performance
Facets: Fast Comprehensive Mining of Coevolving High-order Time Series (YC, HT, WF, PJ, QH), pp. 79–88.
KDDKDD-2015-CostaYTTF #modelling #named #process #social #social media
RSC: Mining and Modeling Temporal Activity in Social Media (AFC, YY, AJMT, CTJ, CF), pp. 269–278.
KDDKDD-2015-FeldmanNPR #online #predict
Utilizing Text Mining on Online Medical Forums to Predict Label Change due to Adverse Drug Reactions (RF, ON, AP, BR), pp. 1779–1788.
KDDKDD-2015-GreenCCMPM
Mining Administrative Data to Spur Urban Revitalization (BG, AC, MC, RM, TP, AM), pp. 1829–1838.
KDDKDD-2015-KimHY #named #topic
TOPTRAC: Topical Trajectory Pattern Mining (YK, JH, CY), pp. 587–596.
KDDKDD-2015-Llinares-LopezS #mutation testing #performance #permutation #testing
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing (FLL, MS, LP, KMB), pp. 725–734.
KDDKDD-2015-RenEWH #approach #automation #corpus #network #recognition #type system
Automatic Entity Recognition and Typing from Massive Text Corpora: A Phrase and Network Mining Approach (XR, AEK, CW, JH), pp. 2319–2320.
KDDKDD-2015-RiondatoU
Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages (MR, EU), pp. 1005–1014.
KDDKDD-2015-SpiliopoulouRR #tutorial
Medical Mining: KDD 2015 Tutorial (MS, PPR, EMR), p. 2325.
KDDKDD-2015-StantonTJVCS #data-driven
Mining for Causal Relationships: A Data-Driven Study of the Islamic State (AS, AT, AJ, PV, AC, PS), pp. 2137–2146.
KDDKDD-2015-WickerKDSBKW0 #data mining #smell
Cinema Data Mining: The Smell of Fear (JW, NK, BD, CS, EB, TK, JW, SK), pp. 1295–1304.
KDDKDD-2015-ZhangW15a #statistics
Statistical Arbitrage Mining for Display Advertising (WZ, JW), pp. 1465–1474.
MLDMMLDM-2015-Al-SaleemAAB #education #performance #predict #student
Mining Educational Data to Predict Students’ Academic Performance (MAS, NAK, SAO, GB), pp. 403–414.
MLDMMLDM-2015-DhulekarNOY #graph #learning #predict
Seizure Prediction by Graph Mining, Transfer Learning, and Transformation Learning (ND, SN, BO, BY), pp. 32–52.
MLDMMLDM-2015-MoldovanM #data mining #learning #performance #using
Learning the Relationship Between Corporate Governance and Company Performance Using Data Mining (DM, SM), pp. 368–381.
MLDMMLDM-2015-Perner #automation #feature model #image
Automatic Cell Tracking and Kinetic Feature Description of Cell Paths for Image Mining (PP), pp. 441–451.
MLDMMLDM-2015-SalahAM #optimisation #performance #pipes and filters
Optimizing the Data-Process Relationship for Fast Mining of Frequent Itemsets in MapReduce (SS, RA, FM), pp. 217–231.
MLDMMLDM-2015-ZidaFWLT #performance
Efficient Mining of High-Utility Sequential Rules (SZ, PFV, CWW, JCWL, VST), pp. 157–171.
SEKESEKE-2015-ChenZWLPW #probability #specification
Mining Universal Specification Based on Probabilistic Model (DC, YZ, RW, XL, LP, WW), pp. 471–476.
SEKESEKE-2015-ChenZWQJW #api #protocol
Extracting More Object Usage Scenarios for API Protocol Mining (DC, YZ, RW, BQ, JJ, WW), pp. 607–612.
SIGIRSIGIR-2015-MaiyaVW
Mining Measured Information from Text (ASM, DV, AW), pp. 899–902.
SIGIRSIGIR-2015-ReinandaMR #aspect-oriented #ranking #recommendation
Mining, Ranking and Recommending Entity Aspects (RR, EM, MdR), pp. 263–272.
SKYSKY-2015-Allalouf #big data #library #visualisation
Big Data in the Library: Extending Modern Library Catalogues with Data Visualization, Linking and Mining (MA), pp. 74–75.
MoDELSMoDELS-2015-LeemansA #distributed #modelling #process #transaction
Process mining in software systems: Discovering real-life business transactions and process models from distributed systems (ML, WMPvdA), pp. 44–53.
ASEASE-2015-LemieuxB #behaviour #ltl #specification #using
Investigating Program Behavior Using the Texada LTL Specifications Miner (CL, IB), pp. 870–875.
ASEASE-2015-LemieuxPB #ltl #specification
General LTL Specification Mining (T) (CL, DP, IB), pp. 81–92.
ASEASE-2015-SorboPVPCG #developer #development #email
Development Emails Content Analyzer: Intention Mining in Developer Discussions (T) (ADS, SP, CAV, MDP, GC, HCG), pp. 12–23.
ASEASE-2015-VuNPN #approach #keyword #mobile
Mining User Opinions in Mobile App Reviews: A Keyword-Based Approach (T) (PMV, TTN, HVP, TTN), pp. 749–759.
ICSEICSE-v1-2015-AvdiienkoKGZARB
Mining Apps for Abnormal Usage of Sensitive Data (VA, KK, AG, AZ, SA, SR, EB), pp. 426–436.
ICSEICSE-v1-2015-Landau #metadata
Mining the Metadata — and Its Consequences (SL), pp. 4–5.
ICSEICSE-v2-2015-Avdiienko
Mining Patterns of Sensitive Data Usage (VA), pp. 891–894.
ICSEICSE-v2-2015-DamSAG #repository #social
Mining Software Repositories for Social Norms (HKD, BTRS, DA, AKG), pp. 627–630.
ICSEICSE-v2-2015-Honsel #evolution #learning #simulation #statistics
Statistical Learning and Software Mining for Agent Based Simulation of Software Evolution (VH), pp. 863–866.
ICSEICSE-v2-2015-Lemieux #invariant
Mining Temporal Properties of Data Invariants (CL), pp. 751–753.
SACSAC-2015-BechetCCC #constraints #multi #sequence
Sequence mining under multiple constraints (NB, PC, TC, BC), pp. 908–914.
SACSAC-2015-BouaskerY #anti #constraints #correlation
Key correlation mining by simultaneous monotone and anti-monotone constraints checking (SB, SBY), pp. 851–856.
SACSAC-2015-Fournier-VigerZ #named #performance
FOSHU: faster on-shelf high utility itemset mining — with or without negative unit profit (PFV, SZ), pp. 857–864.
SACSAC-2015-MergelSS #string #visual notation
A method to support search string building in systematic literature reviews through visual text mining (GDM, MSS, TSdS), pp. 1594–1601.
SACSAC-2015-SantosOA #development #process
Mining software development process variations (RMSS, TCO, FBeA), pp. 1657–1660.
SACSAC-2015-WeberFMS #multi #process
Mining processes with multi-instantiation (IW, MF, JM, JGS), pp. 1231–1237.
SACSAC-2015-ZhangYLC #concept #debugging #predict #repository
Predicting severity of bug report by mining bug repository with concept profile (TZ, GY, BL, ATSC), pp. 1553–1558.
CASECASE-2015-MicioFMBL #automation #process #towards
RTLS-based Process Mining: Towards an automatic process diagnosis in healthcare (RM, FF, GM, PB, ML), pp. 1397–1402.
CASECASE-2015-NouaouriSA #data mining #predict #problem
Evidential data mining for length of stay (LOS) prediction problem (IN, AS, HA), pp. 1415–1420.
CASECASE-2015-ProdelAXJL #database #integer #linear #process #programming #using
Discovery of patient pathways from a national hospital database using process mining and integer linear programming (MP, VA, XX, BJ, LL), pp. 1409–1414.
CGOCGO-2015-NagarajG #analysis #approximate #pointer #using
Approximating flow-sensitive pointer analysis using frequent itemset mining (VN, RG), pp. 225–234.
DATEDATE-2015-FarkashHS #data mining #debugging #locality
Data mining diagnostics and bug MRIs for HW bug localization (MF, BGH, BS), pp. 79–84.
PDPPDP-2015-AltomareCT #data mining #energy #migration #modelling #predict #virtual machine
Energy-Aware Migration of Virtual Machines Driven by Predictive Data Mining Models (AA, EC, DT), pp. 549–553.
SOSPSOSP-2015-TeixeiraFSSZA #distributed #graph #named
Arabesque: a system for distributed graph mining (CHCT, AJF, MS, GS, MJZ, AA), pp. 425–440.
ICLPICLP-2015-Hallen #data mining #higher-order #logic #specification
Higher Order Support in Logic Specification Languages for Data Mining Applications (MvdH), pp. 330–336.
ISSTAISSTA-2015-ClappAA #data flow #named #specification
Modelgen: mining explicit information flow specifications from concrete executions (LC, SA, AA), pp. 129–140.
ICSTSAT-2015-IvriiRS #incremental #satisfiability
Mining Backbone Literals in Incremental SAT — A New Kind of Incremental Data (AI, VR, OS), pp. 88–103.
HTHT-2014-AgarwalS #crawling
A focused crawler for mining hate and extremism promoting videos on YouTube (SA, AS), pp. 294–296.
JCDLJCDL-2014-HarrisLZL
The anatomy of a search and mining system for digital humanities (MH, ML, DZ, DL), pp. 165–168.
JCDLJCDL-2014-LiuYGSG #approach #network #recommendation
Full-text based context-rich heterogeneous network mining approach for citation recommendation (XL, YY, CG, YS, LG), pp. 361–370.
SIGMODSIGMOD-2014-AmsterdamerDMNS #named #query
OASSIS: query driven crowd mining (YA, SBD, TM, SN, AS), pp. 589–600.
SIGMODSIGMOD-2014-AroraSB #graph #statistics
Mining statistically significant connected subgraphs in vertex labeled graphs (AA, MS, AB), pp. 1003–1014.
SIGMODSIGMOD-2014-GalbrunM #interactive
Interactive redescription mining (EG, PM), pp. 1079–1082.
SIGMODSIGMOD-2014-HanW
Mining latent entity structures from massive unstructured and interconnected data (JH, CW), pp. 1409–1410.
SIGMODSIGMOD-2014-MatsubaraSF #automation #co-evolution #named #sequence
AutoPlait: automatic mining of co-evolving time sequences (YM, YS, CF), pp. 193–204.
SIGMODSIGMOD-2014-YamamotoIF #approximate #data type
Resource-oriented approximation for frequent itemset mining from bursty data streams (YY, KI, SF), pp. 205–216.
VLDBVLDB-2014-AmsterdamerDMNS #ontology
Ontology Assisted Crowd Mining (YA, SBD, TM, SN, AS), pp. 1597–1600.
VLDBVLDB-2014-ElseidyASK #graph #named #scalability
GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph (ME, EA, SS, PK), pp. 517–528.
VLDBVLDB-2014-LinMRW #interactive #named
SPIRE: Supporting Parameter-Driven Interactive Rule Mining and Exploration (XL, AM, EAR, MOW), pp. 1653–1656.
VLDBVLDB-2014-WuLLH
MoveMine 2.0: Mining Object Relationships from Movement Data (FW, TKHL, ZL, JH), pp. 1613–1616.
VLDBVLDB-2014-ZhangHSLP #fine-grained #named #semantics
Splitter: Mining Fine-Grained Sequential Patterns in Semantic Trajectories (CZ, JH, LS, JL, TFLP), pp. 769–780.
VLDBVLDB-2015-El-KishkySWVH14 #corpus #scalability #topic
Scalable Topical Phrase Mining from Text Corpora (AEK, YS, CW, CRV, JH), pp. 305–316.
VLDBVLDB-2015-ThirumuruganathanRAD14
Beyond Itemsets: Mining Frequent Featuresets over Structured Items (ST, HR, SA, GD), pp. 257–268.
EDMEDM-2014-CrossleyKVM #assessment #data mining
The Importance of Grammar and Mechanics in Writing Assessment and Instruction: Evidence from Data Mining (SAC, KK, LKV, DSM), pp. 300–303.
EDMEDM-2014-FuentesRGV #data mining #self #student #using
Accepting or Rejecting Students_ Self-grading in their Final Marks by using Data Mining (JF, CR, CGM, SV), pp. 327–328.
EDMEDM-2014-MartinVAMJ #data mining #design #education #research
Microgenetic Designs for Educational Data Mining Research: Poster (TM, NFV, AA, JM, PJ), pp. 387–388.
EDMEDM-2014-MorettiGM14a #data-driven #design #education #web
Data-Driven Curriculum Design: Mining the Web to Make Better Teaching Decisions (AM, JPGB, KM), pp. 421–422.
EDMEDM-2014-MorrisonNSDKR #data mining #database
Building an Intelligent PAL from the Tutor.com Session Database Phase 1: Data Mining (DMM, BN, BS, VVD, CK, VR), pp. 335–336.
EDMEDM-2014-NiraulaRSG #tutorial
Mining Gap-fill Questions from Tutorial Dialogues (NBN, VR, DS, ACG), pp. 265–268.
EDMEDM-2014-PeckhamM #comprehension #education #framework
Mining Reading Comprehension Within Educational Objective Frameworks (TP, GM), pp. 395–396.
EDMEDM-2014-RayBR #data mining #using
Using Data Mining to Automate ADDIE (FR, KWB, RR), pp. 429–430.
EDMEDM-2014-SantosMP #collaboration #learning #student
Mining students' strategies to enable collaborative learning (SGS, MM, AP), pp. 397–398.
EDMEDM-2014-ShuQF #data mining #education #experience #learning #student
Educational Data Mining and Analyzing of Student Learning Outcomes from the Perspective of Learning Experience (ZS, QFQ, LQF), pp. 359–360.
EDMEDM-2014-SilvaA #modelling #multi #student
Mining Multi-dimensional Patterns for Student Modelling (AS0, CA), pp. 393–394.
EDMEDM-2014-SmithWML #abstraction #named #sketching
SKETCHMINER: Mining Learner-Generated Science Drawings with Topological Abstraction (AS, ENW, BWM, JCL), pp. 288–291.
EDMEDM-2014-SyedJG #data mining
Data mining of undergraduate course evaluations (SJS, YHJ, LG), pp. 347–348.
EDMEDM-2014-ValeMA #clustering #education #evolution
Mining coherent evolution patterns in education through biclustering (AV, SCM, CA), pp. 391–392.
EDMEDM-2014-YeKB #identification #learning #multi #process
Mining and Identifying Relationships Among Sequential Patterns in Multi-Feature, Hierarchical Learning Activity Data (CY, JSK, GB), pp. 389–390.
ITiCSEITiCSE-2014-EricssonW #what
Mining job ads to find what skills are sought after from an employers’ perspective on IT graduates (ME, AW), p. 354.
SANERCSMR-WCRE-2014-KashiwabaraOIHYI #recommendation #using
Recommending verbs for rename method using association rule mining (YK, YO, TI, YH, TY, KI), pp. 323–327.
SANERCSMR-WCRE-2014-MondalRS #automation #ranking #refactoring
Automatic ranking of clones for refactoring through mining association rules (MM, CKR, KAS), pp. 114–123.
SANERCSMR-WCRE-2014-OsmanLN #debugging
Mining frequent bug-fix code changes (HO, ML, ON), pp. 343–347.
ICPCICPC-2014-GhafariGMT #recommendation #testing
Mining unit tests for code recommendation (MG, CG, AM, GT), pp. 142–145.
ICPCICPC-2014-VassalloPPC #developer #named #source code
CODES: mining source code descriptions from developers discussions (CV, SP, MDP, GC), pp. 106–109.
ICSMEICSME-2014-McIntoshANH #co-evolution
Mining Co-change Information to Understand When Build Changes Are Necessary (SM, BA, MN, AEH), pp. 241–250.
ICSMEICSME-2014-YanMG #contract #evolution
Mining Software Contracts for Software Evolution (YY, MM, WGG), pp. 471–475.
ICSMEICSME-2014-ZhouTGG #classification #data mining #debugging
Combining Text Mining and Data Mining for Bug Report Classification (YZ, YT, RG, HCG), pp. 311–320.
ICSMEICSME-2014-ZhuZXJL0 #api
Mining API Usage Examples from Test Code (ZZ, YZ, BX, YJ, ZL, LZ), pp. 301–310.
MSRMSR-2014-AndersonSD #effectiveness #testing
Improving the effectiveness of test suite through mining historical data (JA, SS, HD), pp. 142–151.
MSRMSR-2014-BajajPM #developer #web
Mining questions asked by web developers (KB, KP, AM), pp. 112–121.
MSRMSR-2014-GuptaSP #fault #multi #perspective #process #repository
Process mining multiple repositories for software defect resolution from control and organizational perspective (MG, AS, SP), pp. 122–131.
MSRMSR-2014-HindleWRBCR #energy #framework #hardware #named #repository
GreenMiner: a hardware based mining software repositories software energy consumption framework (AH, AW, KR, EJB, JCC, SR), pp. 12–21.
MSRMSR-2014-KalliamvakouGBSGD #git
The promises and perils of mining GitHub (EK, GG, KB, LS, DMG, DD), pp. 92–101.
MSRMSR-2014-KononenkoBHG #repository
Mining modern repositories with elasticsearch (OK, OB, RH, MWG), pp. 328–331.
MSRMSR-2014-Mockus #question #repository
Is mining software repositories data science? (AM), p. 1.
MSRMSR-2014-PintoCL #energy
Mining questions about software energy consumption (GP, FC, YDL), pp. 22–31.
MSRMSR-2014-PonzanelliBPOL #ide #programming #self #stack overflow
Mining StackOverflow to turn the IDE into a self-confident programming prompter (LP, GB, MDP, RO, ML), pp. 102–111.
MSRMSR-2014-RoblesGCCI #case study #development #open source #repository
Estimating development effort in Free/Open source software projects by mining software repositories: a case study of OpenStack (GR, JMGB, CC, AC, DIC), pp. 222–231.
MSRMSR-2014-VasquezBBOPP #android #api #empirical #energy
Mining energy-greedy API usage patterns in Android apps: an empirical study (MLV, GB, CBC, RO, MDP, DP), pp. 2–11.
MSRMSR-2014-ZhangH #dataset #energy
A green miner’s dataset: mining the impact of software change on energy consumption (CZ, AH), pp. 400–403.
CoGCIG-2014-GalliLL #data mining #design pattern
Applying data mining to extract design patterns from Unreal Tournament levels (LG, PLL, DL), pp. 1–8.
ICGTICGT-2014-Bruggink #graph transformation #process #towards
Towards Process Mining with Graph Transformation Systems (HJSB), pp. 253–268.
HCIHCI-AS-2014-Hussain #data mining
Getting the Most from CRM Systems: Data Mining in SugarCRM, Finding Important Patterns (QH), pp. 693–699.
HCIHIMI-DE-2014-AlghamdiT #image #multimodal #retrieval #semantics #towards #using
Towards Semantic Image Retrieval Using Multimodal Fusion with Association Rules Mining (RAA, MT), pp. 407–418.
HCIHIMI-DE-2014-TakahashiA #comparison #comprehension #difference #using
User Needs Search Using the Text Mining — From Commodity Comparison, Understanding the Difference of Users Awareness (YT, YA), pp. 461–467.
CAiSECAiSE-2014-ChanYGM #development #execution #process
Mining Event Logs to Assist the Development of Executable Process Variants (NNC, KY, WG, JM), pp. 548–563.
CAiSECAiSE-2014-EpureHDB #flexibility #process #what
What Shall I Do Next? — Intention Mining for Flexible Process Enactment (EVE, CH, RD, SB), pp. 473–487.
CAiSECAiSE-2014-FolinoGP #low level #modelling #multi #predict #process
Mining Predictive Process Models out of Low-level Multidimensional Logs (FF, MG, LP), pp. 533–547.
CAiSECAiSE-2014-SenderovichWGM #predict #process #queue
Queue Mining — Predicting Delays in Service Processes (AS, MW, AG, AM), pp. 42–57.
CAiSECAiSE-2014-SubeljBKB #process
Automatization of the Stream Mining Process (LS, ZB, MK, MB), pp. 409–423.
EDOCEDOC-2014-Aalst #enterprise #process
Process Mining as the Superglue Between Data Science and Enterprise Computing (WMPvdA), p. 1.
ICEISICEIS-v1-2014-AmaralCRGTS #approach #data mining #framework #image
The SITSMining Framework — A Data Mining Approach for Satellite Image Time Series (BFA, DYTC, LASR, RRdVG, AJMT, EPMdS), pp. 225–232.
ICEISICEIS-v1-2014-AntunesS #data mining #roadmap
New Trends in Knowledge Driven Data Mining (CA, AS), pp. 346–351.
ICEISICEIS-v1-2014-DominguezAERLE #data mining #fuzzy #logic #roadmap #using
Advances in the Decision Making for Treatments of Chronic Patients Using Fuzzy Logic and Data Mining Techniques (MD, JA, JGE, IMR, JMLS, MJE), pp. 325–330.
ICEISICEIS-v1-2014-GuerineRP #data mining #metaheuristic
Extending the Hybridization of Metaheuristics with Data Mining to a Broader Domain (MG, IR, AP), pp. 395–406.
ICEISICEIS-v1-2014-SilvaA #case study #multi
Multi-dimensional Pattern Mining — A Case Study in Healthcare (AS, CA), pp. 273–280.
ICEISICEIS-v2-2014-HernandesTTF #analysis #using #visualisation
Using Visualization and Text Mining to Improve Qualitative Analysis (EMH, ET, ADT, SCPFF), pp. 201–208.
ICEISICEIS-v3-2014-ThabetGG #petri net #process #towards
Towards Business Process Model Extension with Cost Perspective Based on Process Mining — Petri Net Model Case (DT, SAG, HHBG), pp. 335–342.
CIKMCIKM-2014-HsiehL
Mining and Planning Time-aware Routes from Check-in Data (HPH, CTL), pp. 481–490.
CIKMCIKM-2014-LiSWZJTLZZLTL #named
iMiner: Mining Inventory Data for Intelligent Management (LL, CS, LW, LZ, YJ, LT, HL, LZ, CZ, TL, JT, DL), pp. 2057–2059.
CIKMCIKM-2014-LiuXD #network #predict
Relationship Emergence Prediction in Heterogeneous Networks through Dynamic Frequent Subgraph Mining (YL, SX, LD), pp. 1649–1658.
CIKMCIKM-2014-MahdabiC #network #recommendation #retrieval
Query-Driven Mining of Citation Networks for Patent Citation Retrieval and Recommendation (PM, FC), pp. 1659–1668.
CIKMCIKM-2014-SondhiZ #community #knowledge base #natural language #online
Mining Semi-Structured Online Knowledge Bases to Answer Natural Language Questions on Community QA Websites (PS, CZ), pp. 341–350.
CIKMCIKM-2014-WenR #behaviour #identification #online
Identifying Latent Study Habits by Mining Learner Behavior Patterns in Massive Open Online Courses (MW, CPR), pp. 1983–1986.
CIKMCIKM-2014-XuLLZCS
Latent Aspect Mining via Exploring Sparsity and Intrinsic Information (YX, TL, WL, ZZ, HC, AMCS), pp. 879–888.
CIKMCIKM-2014-ZhaoWLZ #framework #refinement #word
A Bootstrapping Based Refinement Framework for Mining Opinion Words and Targets (QZ, HW, PL, CZ), pp. 1995–1998.
ECIRECIR-2014-MarcheggianiTE0 #aspect-oriented #multi #random
Hierarchical Multi-label Conditional Random Fields for Aspect-Oriented Opinion Mining (DM, OT, AE, FS), pp. 273–285.
ICPRICPR-2014-HuLYLL #database #evaluation #multi
Multi-camera Trajectory Mining: Database and Evaluation (YH, SL, DY, ZL, SZL), pp. 4684–4689.
KDDKDD-2014-BordesG #graph #tutorial
Constructing and mining web-scale knowledge graphs: KDD 2014 tutorial (AB, EG), p. 1967.
KDDKDD-2014-Chen0 #big data #documentation #topic
Mining topics in documents: standing on the shoulders of big data (ZC, BL), pp. 1116–1125.
KDDKDD-2014-ChiaS #predict #scalability
Scalable noise mining in long-term electrocardiographic time-series to predict death following heart attacks (CCC, ZS), pp. 125–134.
KDDKDD-2014-Etzioni #data mining #future of
The battle for the future of data mining (OE), p. 1.
KDDKDD-2014-HanWE #topic
Bringing structure to text: mining phrases, entities, topics, and hierarchies (JH, CW, AEK), p. 1968.
KDDKDD-2014-KannanBRFLVAKKUWZ #image #web
Mining text snippets for images on the web (AK, SB, KR, JF, DL, LV, RA, AK, QK, MU, XJW, LZ), pp. 1534–1543.
KDDKDD-2014-KateCPK #named #safety
FoodSIS: a text mining system to improve the state of food safety in singapore (KK, SC, AP, JK), pp. 1709–1718.
KDDKDD-2014-MatsubaraSPF #automation #named
FUNNEL: automatic mining of spatially coevolving epidemics (YM, YS, WGvP, CF), pp. 105–114.
KDDKDD-2014-SpanglerWBNDHRPCMSKLLPLDCL #automation #generative
Automated hypothesis generation based on mining scientific literature (WSS, ADW, BJB, MN, TD, PJH, SR, CRP, AC, JNM, IS, LK, AL, JJL, NP, AML, LAD, YC, OL), pp. 1877–1886.
KDDKDD-2014-TamersoyRC #detection #graph #scalability
Guilt by association: large scale malware detection by mining file-relation graphs (AT, KAR, DHC), pp. 1524–1533.
KDDKDD-2014-XuLGBB #modelling #similarity
Modeling professional similarity by mining professional career trajectories (YX, ZL, AG, AB, AB), pp. 1945–1954.
KDDKDD-2014-XuWCGSKDL #data mining #delivery #network #social
Improving management of aquatic invasions by integrating shipping network, ecological, and environmental data: data mining for social good (JX, TLW, NVC, EKG, KS, RPK, JMD, DML), pp. 1699–1708.
KDDKDD-2014-ZhengZLJXLSZLTLDLW #data mining #optimisation #process
Applying data mining techniques to address critical process optimization needs in advanced manufacturing (LZ, CZ, LL, YJ, WX, JL, CS, WZ, HL, LT, TL, BD, ML, PW), pp. 1739–1748.
KDDKDD-2014-ZhouL #classification #multi #network
Activity-edge centric multi-label classification for mining heterogeneous information networks (YZ, LL), pp. 1276–1285.
KDDKDD-2014-ZhuSY #analysis #network #social
Network mining and analysis for social applications (FZ, HS, XY), p. 1974.
KDDKDD-2014-ZongWSSCHY #scalability #towards
Towards scalable critical alert mining (BZ, YW, JS, AKS, , JH, XY), pp. 1057–1066.
KDIRKDIR-2014-BarcanteJDCCS #identification #using
Identifying Drug Repositioning Targets using Text Mining (EB, MJ, FD, EC, OGC, FS), pp. 348–353.
KDIRKDIR-2014-BenhamouJSS #symmetry
Symmetry Breaking in Itemset Mining (BB, SJ, LS, YS), pp. 86–96.
KDIRKDIR-2014-DuvalCCS #twitter
Mining for Adverse Drug Events on Twitter (FD, EC, OGC, FS), pp. 354–359.
KDIRKDIR-2014-FahedBB #algorithm #predict
Episode Rules Mining Algorithm for Distant Event Prediction (LF, AB, AB), pp. 5–13.
KDIRKDIR-2014-HubwieserM #education #scalability #set
Competency Mining in Large Data Sets — Preparing Large Scale Investigations in Computer Science Education (PH, AM), pp. 315–322.
KDIRKDIR-2014-KurasEAH #data mining #security
The GDR Through the Eyes of the Stasi — Data Mining on the Secret Reports of the State Security Service of the former German Democratic Republic (CK, TE, CA, GH), pp. 360–365.
KDIRKDIR-2014-MorenoGV #data mining #recognition #using #word
Violence Recognition in Spanish Words using Data Mining (AFM, SBGB, JGVR), pp. 210–216.
KDIRKDIR-2014-PassonneauRX #detection #scalability
Company Mention Detection for Large Scale Text Mining (RJP, TR, BX), pp. 512–520.
KDIRKDIR-2014-Rinaldi #database
Text Mining Technologies for Database Curation (FR), pp. 544–548.
KDIRKDIR-2014-TisserantRP #classification #semantics #statistics #twitter
Mining Tweet Data — Statistic and Semantic Information for Political Tweet Classification (GT, MR, VP), pp. 523–529.
KEODKEOD-2014-Aalst #people #process #what
No Knowledge Without Processes — Process Mining as a Tool to Find Out What People and Organizations Really Do (WMPvdA), pp. 1–11.
KEODKEOD-2014-Talia #big data #data mining #distributed #information management
Big Data Mining Services and Distributed Knowledge Discovery Applications on Clouds (DT), pp. 1–5.
KMISKMIS-2014-SelmiHA #feedback #predict
Opinion Mining for Predicting Peer Affective Feedback Helpfulness (MS, HH, EA), pp. 419–425.
KMISKMIS-2014-VelosoPSSRA0 #data mining #modelling #predict #realtime
Real-Time Data Mining Models for Predicting Length of Stay in Intensive Care Units (RV, FP, MFS, ÁMS, FR, AA, JM), pp. 245–254.
MLDMMLDM-2014-DzubaB #music #recommendation
Mining Users Playbacks History for Music Recommendations (AD, DB), pp. 422–430.
MLDMMLDM-2014-KuleshovB #data mining #learning
Manifold Learning in Data Mining Tasks (APK, AVB), pp. 119–133.
MLDMMLDM-2014-SharmaT #social #social media
A Generalized Relationship Mining Method for Social Media Text Data (TS, DT), pp. 376–392.
MLDMMLDM-2014-WangLK #concurrent #data mining
Applications of Concurrent Sequential Patterns in Protein Data Mining (CW, JL, MK), pp. 243–257.
RecSysRecSys-2014-JannachF #data mining #modelling #process #recommendation
Recommendation-based modeling support for data mining processes (DJ, SF), pp. 337–340.
RecSysRecSys-2014-SureshRE #recommendation
Aspect-based opinion mining and recommendationsystem for restaurant reviews (VS, SR, ME), pp. 361–362.
SEKESEKE-2014-KaurKF #low cost #using #web
Using Web Mining to Support Low Cost Historical Vehicle Traffic Analytics (CK, DK, BHF), pp. 365–370.
OnwardOnward-2014-SpasojevicLN #ecosystem #type inference
Mining the Ecosystem to Improve Type Inference for Dynamically Typed Languages (BS, ML, ON), pp. 133–142.
RERE-2014-GervasiZ #traceability
Supporting traceability through affinity mining (VG, DZ), pp. 143–152.
ASEASE-2014-NguyenNNN #api #approach #learning #migration #statistics
Statistical learning approach for mining API usage mappings for code migration (ATN, HAN, TTN, TNN), pp. 457–468.
FSEFSE-2014-AllamanisS #source code
Mining idioms from source code (MA, CAS), pp. 472–483.
FSEFSE-2014-KrkaBM #automation #invariant #specification
Automatic mining of specifications from invocation traces and method invariants (IK, YB, NM), pp. 178–189.
FSEFSE-2014-Nguyen0NR #api #corpus #scalability
Mining preconditions of APIs in large-scale code corpus (HAN, RD, TNN, HR), pp. 166–177.
FSEFSE-2014-ZhouM
Mining micro-practices from operational data (MZ, AM), pp. 845–848.
ICSEICSE-2014-ChenLHXZ #developer #mobile #named
AR-miner: mining informative reviews for developers from mobile app marketplace (NC, JL, SCHH, XX, BZ), pp. 767–778.
ICSEICSE-2014-GhezziPST #behaviour #modelling #web
Mining behavior models from user-intensive web applications (CG, MP, MS, GT), pp. 277–287.
ICSEICSE-2014-NadiBKC #analysis #constraints #empirical
Mining configuration constraints: static analyses and empirical results (SN, TB, CK, KC), pp. 140–151.
ICSEICSE-2014-NegaraCDJ #detection #fine-grained
Mining fine-grained code changes to detect unknown change patterns (SN, MC, DD, REJ), pp. 803–813.
ICSEICSE-2014-NguyenNNN #interprocedural #javascript #web
Mining interprocedural, data-oriented usage patterns in JavaScript web applications (HVN, HAN, ATN, TNN), pp. 791–802.
SACSAC-2014-EvermannA #algorithm #big data #implementation #process
Big data meets process mining: implementing the alpha algorithm with map-reduce (JE, GA), pp. 1414–1416.
SACSAC-2014-LiuTZ #constraints
Mining key and referential constraints enforcement patterns (KL, HBKT, HZ), pp. 850–854.
CASECASE-2014-ChangYLC #behaviour #towards #video
Toward mining anomalous behavior from big moving trajectories in surveillance video (CWC, MHY, CCL, KTC), pp. 1121–1126.
CASECASE-2014-HajovskyPOH #energy
Heat energy collection from thermally active mining dump Hedvika (RH, MP, SO, JH), pp. 44–49.
CASECASE-2014-HungCYDYW #behaviour #data mining
Data mining for analysing kiosk usage behavior patterns (YSH, KLBC, CTY, GFD, YHY, NCW), pp. 1115–1120.
DACDAC-2014-LinWC #data mining #design #logic #named #power management #synthesis
C-Mine: Data Mining of Logic Common Cases for Low Power Synthesis of Better-Than-Worst-Case Designs (CHL, LW, DC), p. 6.
DACDAC-2014-WangA #constraints #data mining
Data Mining In EDA — Basic Principles, Promises, and Constraints (LCW, MSA), p. 6.
DATEDATE-2014-FerentD #comparison #novel #synthesis #using
Novel circuit topology synthesis method using circuit feature mining and symbolic comparison (CF, AD), pp. 1–4.
DATEDATE-2014-LagraaTP #data mining #scalability #simulation #using
Scalability bottlenecks discovery in MPSoC platforms using data mining on simulation traces (SL, AT, FP), pp. 1–6.
FASEFASE-2014-SadeghiEM #analysis #repository #security
Mining the Categorized Software Repositories to Improve the Analysis of Security Vulnerabilities (AS, NE, SM), pp. 155–169.
ISSTAISSTA-2014-SchurRZ #modelling #multi #named #web
ProCrawl: mining test models from multi-user web applications (MS, AR, AZ), pp. 413–416.
ISSTAISSTA-2014-ZuoKS #debugging #performance
Efficient predicated bug signature mining via hierarchical instrumentation (ZZ, SCK, CS), pp. 215–224.
CBSECBSE-2013-KarveS #component #online #towards
Towards mining informal online data to guide component-reuse decisions (SK, CS), pp. 65–74.
JCDLJCDL-2013-AfiontziKPSTP #evaluation #library #semantics
Charting the digital library evaluation domain with a semantically enhanced mining methodology (EA, GK, LP, MS, GT, CP), pp. 125–134.
PODSPODS-2013-KimelfeldK #complexity
The complexity of mining maximal frequent subgraphs (BK, PGK), pp. 13–24.
SIGMODSIGMOD-2013-AchtertKSZ #3d #coordination #data mining #interactive
Interactive data mining with 3D-parallel-coordinate-trees (EA, HPK, ES, AZ), pp. 1009–1012.
SIGMODSIGMOD-2013-AmsterdamerGMS
Crowd mining (YA, YG, TM, PS), pp. 241–252.
SIGMODSIGMOD-2013-GuilleFHZ #analysis #framework #named #open source #platform #social
SONDY: an open source platform for social dynamics mining and analysis (AG, CF, HH, DAZ), pp. 1005–1008.
SIGMODSIGMOD-2013-LappasVGT #named #process
STEM: a spatio-temporal miner for bursty activity (TL, MRV, DG, VJT), pp. 1021–1024.
SIGMODSIGMOD-2013-MiliarakiBGZ #scalability #sequence
Mind the gap: large-scale frequent sequence mining (IM, KB, RG, SZ), pp. 797–808.
SIGMODSIGMOD-2013-MukherjiLBWRWR #interactive #named #parametricity
PARAS: interactive parameter space exploration for association rule mining (AM, XL, CRB, JW, EAR, MOW, CR), pp. 1017–1020.
SIGMODSIGMOD-2013-OrdonezGGCBQ #algorithm #as a service #data mining #database #in the cloud #relational
Data mining algorithms as a service in the cloud exploiting relational database systems (CO, JGG, CGA, WC, VB, MSQ), pp. 1001–1004.
SIGMODSIGMOD-2013-ZhuZQ #approach #graph #performance
A direct mining approach to efficient constrained graph pattern discovery (FZ, ZZ, QQ), pp. 821–832.
VLDBVLDB-2013-AmsterdamerGMS #named
CrowdMiner: Mining association rules from the crowd (YA, YG, TM, PS), pp. 1250–1253.
VLDBVLDB-2013-Bonomi #difference #privacy
Mining Frequent Patterns with Differential Privacy (LB), pp. 1422–1427.
VLDBVLDB-2013-LinMRRW #framework #named #online #parametricity
PARAS: A Parameter Space Framework for Online Association Mining (XL, AM, EAR, CR, MOW), pp. 193–204.
VLDBVLDB-2013-MousaviGZ #database #knowledge base #named
IBminer: A Text Mining Tool for Constructing and Populating InfoBox Databases and Knowledge Bases (HM, SG, CZ), pp. 1330–1333.
VLDBVLDB-2013-YangZHLRW #data type
Mining and Linking Patterns across Live Data Streams and Stream Archives (DY, KZ, MH, HL, EAR, MOW), pp. 1346–1349.
VLDBVLDB-2013-YuanMG #graph
Mining and Indexing Graphs for Supergraph Search (DY, PM, CLG), pp. 829–840.
ITiCSEITiCSE-2013-MedinaPGR #data mining #education #learning #programming #using
Assistance in computer programming learning using educational data mining and learning analytics (CFM, JRPP, VMÁG, MdPPR), pp. 237–242.
ICSMEICSM-2013-LarssonH #debugging
Mining Telecom System Logs to Facilitate Debugging Tasks (AL, AHL), pp. 536–539.
ICSMEICSM-2013-MengMWB #repository
Mining Software Repositories for Accurate Authorship (XM, BPM, WRW, ARB), pp. 250–259.
ICSMEICSM-2013-QianPXJZ #logic #programming
Mining Logical Clones in Software: Revealing High-Level Business and Programming Rules (WQ, XP, ZX, SJ, WZ), pp. 40–49.
ICSMEICSM-2013-WangWYLLZ #categorisation #multi #repository
Mining Software Profile across Multiple Repositories for Hierarchical Categorization (TW, HW, GY, CXL, XL, PZ), pp. 240–249.
MSRMSR-2013-AllamanisS13a #modelling #repository #source code #using
Mining source code repositories at massive scale using language modeling (MA, CAS), pp. 207–216.
MSRMSR-2013-GhezziG
Replicating mining studies with SOFAS (GG, HCG), pp. 363–372.
MSRMSR-2013-HemmatiNBKWHG #research
The MSR cookbook: mining a decade of research (HH, SN, OB, OK, WW, RH, MWG), pp. 343–352.
MSRMSR-2013-HowardGPV #automation #semantics #word
Automatically mining software-based, semantically-similar words from comment-code mappings (MJH, SG, LLP, KVS), pp. 377–386.
MSRMSR-2013-LamkanfiPD #dataset #debugging #eclipse #fault
The eclipse and mozilla defect tracking dataset: a genuine dataset for mining bug information (AL, JP, SD), pp. 203–206.
MSRMSR-2013-WangDZCXZ #api #source code
Mining succinct and high-coverage API usage patterns from source code (JW, YD, HZ, KC, TX, DZ), pp. 319–328.
PASTEPASTE-2013-Lu #automation
Automatically mining program build information via signature matching (CDL), pp. 25–32.
SCAMSCAM-2013-SokolAG #named #repository #research
MetricMiner: Supporting researchers in mining software repositories (FZS, MFA, MAG), pp. 142–146.
WCREWCRE-2013-HoraADV
Mining system specific rules from change patterns (AH, NA, SD, MTV), pp. 331–340.
WCREWCRE-2013-JaafarGHK #anti #dependence
Mining the relationship between anti-patterns dependencies and fault-proneness (FJ, YGG, SH, FK), pp. 351–360.
WCREWCRE-2013-NayrollesMV #detection #execution
Improving SOA antipatterns detection in Service Based Systems by mining execution traces (MN, NM, PV), pp. 321–330.
CoGCIG-2013-ChoK #comparison #data mining
Comparison of human and AI bots in StarCraft with replay data mining (HCC, KJK), pp. 1–2.
CHICHI-2013-KumarSTLAKT #design #named #web
Webzeitgeist: design mining the web (RK, AS, CT, ML, SA, SRK, JOT), pp. 3083–3092.
CSCWCSCW-2013-ChoiHHLS #communication #social #using
Mining social relationship types in an organization using communication patterns (JC, SH, JH, GL, JS), pp. 295–302.
CSCWCSCW-2013-MinWHZ #smarttech #social
Mining smartphone data to classify life-facets of social relationships (JKM, JW, JIH, JZ), pp. 285–294.
HCIDUXU-WM-2013-LiuVMM #big data #design #experience #framework #interactive #platform #visualisation
Designing Discovery Experience for Big Data Interaction: A Case of Web-Based Knowledge Mining and Interactive Visualization Platform (QL, MV, KPCM, AFM), pp. 543–552.
HCIHIMI-LCCB-2013-SatonakaS #visualisation
Sales Strategy Mining System with Visualization of Action History (HS, WS), pp. 588–597.
HCIHIMI-LCCB-2013-TakahashiA #using
User Needs Search Using Text Mining (YT, YA), pp. 607–615.
HCIHIMI-LCCB-2013-YuL #approach #feedback
Exploring User Feedback of a E-Learning System: A Text Mining Approach (WBY, RL), pp. 182–191.
HCIOCSC-2013-Abascal-MenaLZ #analysis #behaviour #twitter
User Generated Content: An Analysis of User Behavior by Mining Political Tweets (RAM, ÉLO, JSZH), pp. 3–12.
ICEISICEIS-J-2013-KalsingITN13a #incremental #legacy #modelling #process #using
Re-learning of Business Process Models from Legacy System Using Incremental Process Mining (ACK, CI, LHT, GSdN), pp. 314–330.
ICEISICEIS-v1-2013-al-KetbiC #data mining #integration
Integration of Decision Support Systems and Data Mining for Improved Decision Making (OaK, MC), pp. 482–489.
ICEISICEIS-v1-2013-SonodaM #statistics
Mining Japanese Collocation by Statistical Indicators (TS, TM), pp. 381–388.
ICEISICEIS-v2-2013-KalsingITN #incremental #learning #legacy #modelling #process #using
Evolutionary Learning of Business Process Models from Legacy Systems using Incremental Process Mining (ACK, CI, LHT, GSdN), pp. 58–69.
CIKMCIKM-2013-BonomiX #algorithm #difference #privacy
A two-phase algorithm for mining sequential patterns with differential privacy (LB, LX), pp. 269–278.
CIKMCIKM-2013-BrossE #automation #overview #sentiment
Automatic construction of domain and aspect specific sentiment lexicons for customer review mining (JB, HE), pp. 1077–1086.
CIKMCIKM-2013-Giles #big data #data mining #information management
Scholarly big data: information extraction and data mining (CLG), pp. 1–2.
CIKMCIKM-2013-HanW #graph #scalability
Mining frequent neighborhood patterns in a large labeled graph (JH, JRW), pp. 259–268.
CIKMCIKM-2013-JabbourSS #satisfiability #sequence
Boolean satisfiability for sequence mining (SJ, LS, YS), pp. 649–658.
CIKMCIKM-2013-KimCHZRD #feedback #modelling #topic
Mining causal topics in text data: iterative topic modeling with time series feedback (HDK, MC, MH, CZ, TAR, DD), pp. 885–890.
CIKMCIKM-2013-LiaoPPL #behaviour #mobile #on the #predict #smarttech
On mining mobile apps usage behavior for predicting apps usage in smartphones (ZXL, YCP, WCP, PRL), pp. 609–618.
CIKMCIKM-2013-LiHZW #clustering
Mining entity attribute synonyms via compact clustering (YL, BJPH, CZ, KW), pp. 867–872.
CIKMCIKM-2013-LiuLAM #personalisation #recommendation
Personalized point-of-interest recommendation by mining users’ preference transition (XL, YL, KA, CM), pp. 733–738.
CIKMCIKM-2013-LiuTCALC
Mining diabetes complication and treatment patterns for clinical decision support (LL, JT, YC, AA, WkL, ANC), pp. 279–288.
CIKMCIKM-2013-QianSYZL #query
Dynamic query intent mining from a search log stream (YnQ, TS, JY, QZ, CL), pp. 1205–1208.
CIKMCIKM-2013-SubbianAS #analysis #social
Content-centric flow mining for influence analysis in social streams (KS, CCA, JS), pp. 841–846.
CIKMCIKM-2013-Zhang0D #corpus #query
Mining a search engine’s corpus without a query pool (MZ, NZ, GD), pp. 29–38.
ECIRECIR-2013-GalitskyK #web
A Web Mining Tool for Assistance with Creative Writing (BG, SOK), pp. 828–831.
KDDKDD-2013-AnchuriZBGS #approximate #graph
Approximate graph mining with label costs (PA, MJZ, OB, SG, MS), pp. 518–526.
KDDKDD-2013-ChengXCACG #named #realtime #social #social media
JobMiner: a real-time system for mining job-related patterns from social media (YC, YX, ZC, AA, ANC, SG), pp. 1450–1453.
KDDKDD-2013-DanilevskyWTNCDWH #named #semistructured data #topic
AMETHYST: a system for mining and exploring topical hierarchies of heterogeneous data (MD, CW, FT, SN, GC, ND, LW, JH), pp. 1458–1461.
KDDKDD-2013-EmersonWN #data mining #profiling
A data mining driven risk profiling method for road asset management (DE, JW, RN), pp. 1267–1275.
KDDKDD-2013-FriezeGT #algorithm #graph #modelling #scalability
Algorithmic techniques for modeling and mining large graphs (AMAzING) (AMF, AG, CET), p. 1523.
KDDKDD-2013-KaramshukNSNM #named #online
Geo-spotting: mining online location-based services for optimal retail store placement (DK, AN, SS, VN, CM), pp. 793–801.
KDDKDD-2013-KongCY #classification #correlation #multi #network
Multi-label classification by mining label and instance correlations from heterogeneous information networks (XK, BC, PSY), pp. 614–622.
KDDKDD-2013-LiWHHRY #ambiguity
Mining evidences for named entity disambiguation (YL, CW, FH, JH, DR, XY), pp. 1070–1078.
KDDKDD-2013-PapadimitriouE #mobile #overview
Mining data from mobile devices: a survey of smart sensing and analytics (SP, TER), p. 1524.
KDDKDD-2013-RanuHS #network
Mining discriminative subgraphs from global-state networks (SR, MXH, AKS), pp. 509–517.
KDDKDD-2013-RautiainenSHYK #concept #online
An online system with end-user services: mining novelty concepts from tv broadcast subtitles (MR, JS, AH, MY, VK), pp. 1486–1489.
KDDKDD-2013-Schadt #personalisation
Mining the digital universe of data to develop personalized cancer therapies (EES), p. 1134.
KDDKDD-2013-ShakarianRCK #community #composition #distance #network #social
Mining for geographically disperse communities in social networks by leveraging distance modularity (PS, PR, DC, CK), pp. 1402–1409.
KDDKDD-2013-ShenY #difference #graph #privacy
Mining frequent graph patterns with differential privacy (ES, TY), pp. 545–553.
KDDKDD-2013-TangYGHLP #cyber-physical
Mining lines in the sand: on trajectory discovery from untrustworthy data in cyber-physical system (LAT, XY, QG, JH, AL, TFLP), pp. 410–418.
KDDKDD-2013-TaoLHZCDDDGJKKLLLLOSTWZZ #multi #named
EventCube: multi-dimensional search and mining of structured and text data (FT, KHL, JH, CZ, XC, MD, ND, BD, JG, HJ, RK, AK, QL, YL, CXL, JL, NCO, ANS, RT, CW, DZ, BZ), pp. 1494–1497.
KDDKDD-2013-WangDDZNTH #framework #recursion #topic
A phrase mining framework for recursive construction of a topical hierarchy (CW, MD, ND, YZ, PN, TT, JH), pp. 437–445.
KDDKDD-2013-WangDYWCSI #clustering #data mining #framework #identification #towards
Towards long-lead forecasting of extreme flood events: a data mining framework for precipitation cluster precursors identification (DW, WD, KY, XW, PC, DLS, SI), pp. 1285–1293.
KDDKDD-2013-WangLSG #multi
Mining evolutionary multi-branch trees from text streams (XW, SL, YS, BG), pp. 722–730.
KDDKDD-2013-WuLYT #sequence
Mining high utility episodes in complex event sequences (CWW, YFL, PSY, VST), pp. 536–544.
KDDKDD-2013-ZengJZLLLSZLDLW #data mining #distributed #named #performance
FIU-Miner: a fast, integrated, and user-friendly system for data mining in distributed environment (CZ, YJ, LZ, JL, LL, HL, CS, WZ, TL, BD, ML, PW), pp. 1506–1509.
KDIRKDIR-KMIS-2013-GengUA #algorithm
Trajectory Pattern Mining in Practice — Algorithms for Mining Flock Patterns from Trajectories (XG, TU, HA), pp. 143–151.
KDIRKDIR-KMIS-2013-MeiselBI #query
Mining the Long Tail of Search Queries — Finding Profitable Patterns (MM, MB, AI), pp. 225–229.
KDIRKDIR-KMIS-2013-PereiraRT #data mining #source code #student
Extraction Student Dropout Patterns with Data Mining Techniques in Undergraduate Programs (RTP, ACR, JJT), pp. 136–142.
KDIRKDIR-KMIS-2013-PfeiferP #taxonomy
Mapping Text Mining Taxonomies (KP, EP), pp. 5–16.
KDIRKDIR-KMIS-2013-RauchS #case study #using
Using Domain Knowledge in Association Rules Mining — Case Study (JR, MS), pp. 104–111.
MLDMMLDM-2013-AllahSG #algorithm #array #dataset #performance #scalability
An Efficient and Scalable Algorithm for Mining Maximal — High Confidence Rules from Microarray Dataset (WZAA, YKES, FFMG), pp. 352–366.
MLDMMLDM-2013-DittakanCC #case study #comparative #image
Satellite Image Mining for Census Collection: A Comparative Study with Respect to the Ethiopian Hinterland (KD, FC, RC), pp. 260–274.
MLDMMLDM-2013-GaoD #data mining #distributed #performance #using
Improving the Efficiency of Distributed Data Mining Using an Adjustment Work Flow (JG, JD), pp. 69–83.
MLDMMLDM-2013-LiM #community #network #topic
Mining Groups of Common Interest: Discovering Topical Communities with Network Flows (LL, NDM), pp. 405–420.
MLDMMLDM-2013-ValencioKMSM #3d #data mining #visualisation
3D Geovisualisation Techniques Applied in Spatial Data Mining (CRV, TK, CAdM, RCGdS, JMM), pp. 57–68.
SEKESEKE-2013-Al-MsiedeenSHUVS #concept analysis #object-oriented #semantics #source code #using
Mining Features from the Object-Oriented Source Code of a Collection of Software Variants Using Formal Concept Analysis and Latent Semantic Indexing (RAM, ADS, MH, CU, SV, HES), pp. 244–249.
SEKESEKE-2013-DelfimG #coordination #multi #slicing #using
Multiple Coordinated Views to Support Aspect Mining Using Program Slicing (FMD, REG), pp. 531–536.
SEKESEKE-2013-MaffortVBHAM #design pattern #using
Mining Architectural Patterns Using Association Rules (CAM, MTV, RdSB, AH, NA, JM), pp. 375–380.
SEKESEKE-2013-MarcalGCJ #identification #repository #using
Mining Software Repository to Identify Crosscutting Concerns Using Combined Techniques (IM, REG, RCMC, COJ), pp. 338–343.
SEKESEKE-2013-PytelBG #validation
Proposal and Validation of a Feasibility Model for Information Mining Projects (PP, PB, RGM), pp. 83–88.
SIGIRSIGIR-2013-GuoJLYA #interactive #mobile #predict #web
Mining touch interaction data on mobile devices to predict web search result relevance (QG, HJ, DL, SY, EA), pp. 153–162.
SIGIRSIGIR-2013-JiangN #topic #web
Mining web search topics with diverse spatiotemporal patterns (DJ, WN), pp. 881–884.
SIGIRSIGIR-2013-SakaiDYLZKSI #summary #topic
Summary of the NTCIR-10 INTENT-2 task: subtopic mining and search result diversification (TS, ZD, TY, YL, MZ, MPK, RS, MI), pp. 761–764.
PLEASEPLEASE-2013-HuangYKHU #analysis #domain-specific language #repository #towards
Domain analysis for mining software repositories: Towards feature-based DSL construction (CH, KY, YK, KH, NU), pp. 41–44.
SPLCSPLC-2013-FerrariSd #documentation #natural language
Mining commonalities and variabilities from natural language documents (AF, GOS, FD), pp. 116–120.
SASSAS-2013-PelegSYY #automaton #specification
Symbolic Automata for Static Specification Mining (HP, SS, EY, HY), pp. 63–83.
RERE-2013-MasseyEAS #analysis #automation #documentation #policy #requirements
Automated text mining for requirements analysis of policy documents (AKM, JE, AIA, PPS), pp. 4–13.
ASEASE-2013-FahlandLM
Mining branching-time scenarios (DF, DL, SM), pp. 443–453.
ASEASE-2013-RegerBR #approach #parametricity #specification
A pattern-based approach to parametric specification mining (GR, HB, DER), pp. 658–663.
ASEASE-2013-WongYT #automation #generative #named
AutoComment: Mining question and answer sites for automatic comment generation (EW, JY, LT), pp. 562–567.
ESEC-FSEESEC-FSE-2013-SchurRZ #behaviour #enterprise #modelling #web
Mining behavior models from enterprise web applications (MS, AR, AZ), pp. 422–432.
ESEC-FSEESEC-FSE-2013-SunK #debugging
Mining succinct predicated bug signatures (CS, SCK), pp. 576–586.
ICSEICSE-2013-BettenburgB #development
Deciphering the story of software development through frequent pattern mining (NB, AB), pp. 1197–1200.
ICSEICSE-2013-SharTB #hybrid #injection #program analysis #sql #using
Mining SQL injection and cross site scripting vulnerabilities using hybrid program analysis (LKS, HBKT, LCB), pp. 642–651.
SACSAC-2013-AccorsiSM #on the #process #security
On the exploitation of process mining for security audits: the process discovery case (RA, TS, GM), pp. 1462–1468.
SACSAC-2013-CameronCL #memory management #set
Stream mining of frequent sets with limited memory (JJC, AC, CKSL), pp. 173–175.
SACSAC-2013-DurelliSADC #overview
A systematic review on mining techniques for crosscutting concerns (RSD, DSMS, NA, MED, VVdC), pp. 1080–1087.
SACSAC-2013-Fournier-VigerT #named
TNS: mining top-k non-redundant sequential rules (PFV, VST), pp. 164–166.
SACSAC-2013-LeoniA #process #using
Data-aware process mining: discovering decisions in processes using alignments (MdL, WMPvdA), pp. 1454–1461.
SACSAC-2013-LiZSL #approach #graph #named #novel
WAVE-CIA: a novel CIA approach based on call graph mining (BL, QZ, XS, HL), pp. 1000–1005.
SACSAC-2013-MaunzVH #graph
Out-of-bag discriminative graph mining (AM, DV, CH), pp. 109–114.
SACSAC-2013-ZhangHMZMM #data type
Mining frequent itemsets over tuple-evolving data streams (CZ, YH, MM, CZ, HM, FM), pp. 267–274.
GPCEGPCE-2013-0001RN #abstract syntax tree #declarative #fine-grained #source code
Declarative visitors to ease fine-grained source code mining with full history on billions of AST nodes (RD, HR, TNN), pp. 23–32.
DATEDATE-2013-LagraaTP #concurrent #data access #data mining #identification #memory management #simulation
Data mining MPSoC simulation traces to identify concurrent memory access patterns (SL, AT, FP), pp. 755–760.
FASEFASE-2013-SanthiarPK #api #testing
Discovering Math APIs by Mining Unit Tests (AS, OP, AK), pp. 327–342.
ICTSSICTSS-2013-ElyasovPH #algebra #specification
Guided Algebraic Specification Mining for Failure Simplification (AE, ISWBP, JH), pp. 223–238.
DocEngDocEng-2012-WidlocherM #corpus #framework #platform
The Glozz platform: a corpus annotation and mining tool (AW, YM), pp. 171–180.
SIGMODSIGMOD-2012-AkogluCKKF #graph #named #scalability #visualisation
OPAvion: mining and visualization in large graphs (LA, DHC, UK, DK, CF), pp. 717–720.
SIGMODSIGMOD-2012-FaloutsosK #algorithm #graph #scalability
Managing and mining large graphs: patterns and algorithms (CF, UK), pp. 585–588.
SIGMODSIGMOD-2012-MongioviBRSPF #named #network
SigSpot: mining significant anomalous regions from time-evolving networks (MM, PB, RR, AKS, EEP, CF), p. 865.
SIGMODSIGMOD-2012-ShaoWX #graph #implementation #scalability
Managing and mining large graphs: systems and implementations (BS, HW, YX), pp. 589–592.
VLDBVLDB-2012-LiQSC #difference #named #privacy
PrivBasis: Frequent Itemset Mining with Differential Privacy (NL, WHQ, DS, JC), pp. 1340–1351.
VLDBVLDB-2012-SachanB #statistics #string #using
Mining Statistically Significant Substrings using the Chi-Square Statistic (MS, AB), pp. 1052–1063.
VLDBVLDB-2012-SilvaMZ #correlation #graph #scalability
Mining Attribute-structure Correlated Patterns in Large Attributed Graphs (AS, WMJ, MJZ), pp. 466–477.
VLDBVLDB-2012-SunHYY #analysis #approach #network
Mining Knowledge from Interconnected Data: A Heterogeneous Information Network Analysis Approach (YS, JH, XY, PSY), pp. 2022–2023.
VLDBVLDB-2012-TongCCY #database #nondeterminism
Mining Frequent Itemsets over Uncertain Databases (YT, LC, YC, PSY), pp. 1650–1661.
VLDBVLDB-2013-ZengNC12 #on the
On differentially private frequent itemset mining (CZ, JFN, JYC), pp. 25–36.
WCREWCRE-J-2009-LoRRV12 #algorithm #evaluation #quantifier
Mining quantified temporal rules: Formalism, algorithms, and evaluation (DL, GR, VPR, KV), pp. 743–759.
CSMRCSMR-2012-HashimotoM #analysis #fine-grained
Enhancing History-Based Concern Mining with Fine-Grained Change Analysis (MH, AM), pp. 75–84.
CSMRCSMR-2012-NadiH #detection #linux #variability
Mining Kbuild to Detect Variability Anomalies in Linux (SN, RCH), pp. 107–116.
CSMRCSMR-2012-PetersZ #repository #smell #using
Evaluating the Lifespan of Code Smells using Software Repository Mining (RP, AZ), pp. 411–416.
ICPCICPC-2012-PanichellaAPMC #developer #source code
Mining source code descriptions from developer communications (SP, JA, MDP, AM, GC), pp. 63–72.
MSRMSR-2012-BaysalHG #development
Mining usage data and development artifacts (OB, RH, MWG), pp. 98–107.
MSRMSR-2012-Breckel #comparison #database #debugging #detection #fault #scalability
Error mining: Bug detection through comparison with large code databases (AB), pp. 175–178.
MSRMSR-2012-HarmanJZ #analysis
App store mining and analysis: MSR for app stores (MH, YJ, YZ), pp. 108–111.
MSRMSR-2012-Hindle #power management
Green mining: A methodology of relating software change to power consumption (AH), pp. 78–87.
MSRMSR-2012-KeivanlooFHENPR #framework #linked data #open data #platform #repository
A Linked Data platform for mining software repositories (IK, CF, AH, ME, CN, GP, JR), pp. 32–35.
MSRMSR-2012-ReinaR #android #locality
Mining for localization in Android (LAR, GR), pp. 136–139.
MSRMSR-2012-ShihabKB #android #challenge #framework #platform
Mining challenge 2012: The Android platform (ES, YK, PB), pp. 112–115.
MSRMSR-2012-SinhaMG #android #named
MINCE: Mining change history of Android project (VSS, SM, MG), pp. 132–135.
WCREWCRE-2012-TeytonFB #graph #library #migration
Mining Library Migration Graphs (CT, JRF, XB), pp. 289–298.
WCREWCRE-2012-ZiftciK #data mining #feature model #using
Feature Location Using Data Mining on Existing Test-Cases (CZ, IK), pp. 155–164.
IFLIFL-2012-WuMBS #case study #experience #lazy evaluation
Pure and Lazy λ Mining — An Experience Report (NW, JPM, JB, WS), pp. 207–223.
AIIDEAIIDE-2012-GowCCM #experience #process
Mining Rules from Player Experience and Activity Data (JG, SC, PAC, PM).
CAiSECAiSE-2012-EngelAZPW #case study #modelling #process
Mining Inter-organizational Business Process Models from EDI Messages: A Case Study from the Automotive Sector (RE, WMPvdA, MZ, CP, HW), pp. 222–237.
CAiSECAiSE-2012-LyIMR #data transformation #process #semantics
Data Transformation and Semantic Log Purging for Process Mining (LTL, CI, JM, SRM), pp. 238–253.
ICEISICEIS-J-2012-AyresS12a #similarity
Exploring Similarity Relations According to Different Contexts in Mining Generalized Association Rules (RMJA, MTPS), pp. 137–152.
ICEISICEIS-J-2012-FabbriHTBZS12a #using #visualisation
Using Information Visualization and Text Mining to Facilitate the Conduction of Systematic Literature Reviews (SF, EMH, ADT, AB, AZ, CS), pp. 243–256.
ICEISICEIS-v1-2012-AyresS #fuzzy #ontology #similarity #using
Mining Generalized Association Rules using Fuzzy Ontologies with Context-based Similarity (RMJA, MTPS), pp. 74–83.
ICEISICEIS-v1-2012-ZianiRO #approach #constraints #multi
A Constraint-based Mining Approach for Multi-attribute Index Selection (BZ, FR, YO), pp. 93–98.
ICEISICEIS-v2-2012-LukaszewskiJL #data mining #ontology #semantics #using
Attribute Value Ontology — Using Semantics in Data Mining (TL, JJ, AL), pp. 329–334.
CIKMCIKM-2012-AmiriC #sentiment
Mining sentiment terminology through time (HA, TSC), pp. 2060–2064.
CIKMCIKM-2012-BhuiyanMH #interactive
Interactive pattern mining on hidden data: a sampling-based solution (MB, SM, MAH), pp. 95–104.
CIKMCIKM-2012-DaiZLP #web
Mining coherent anomaly collections on web data (HD, FZ, EPL, HP), pp. 1557–1561.
CIKMCIKM-2012-DongFSZY #framework
A general framework to encode heterogeneous information sources for contextual pattern mining (WD, WF, LS, CZ, XY), pp. 65–74.
CIKMCIKM-2012-HaiCC
One seed to find them all: mining opinion features via association (ZH, KC, GC), pp. 255–264.
CIKMCIKM-2012-HuHXLUZ #analysis
Finding nuggets in IP portfolios: core patent mining through textual temporal analysis (PH, MH, PX, WL, AKU, XZ), pp. 1819–1823.
CIKMCIKM-2012-IntagornL #approach #probability #social
A probabilistic approach to mining geospatial knowledge from social annotations (SI, KL), pp. 1717–1721.
CIKMCIKM-2012-KacimiG #named
MOUNA: mining opinions to unveil neglected arguments (MK, JG), pp. 2722–2724.
CIKMCIKM-2012-KimZRDHCL #named #topic
InCaToMi: integrative causal topic miner between textual and non-textual time series data (HDK, CZ, TAR, DD, MH, MC, CCL), pp. 2689–2691.
CIKMCIKM-2012-KozawaAK #database #gpu #nondeterminism #probability
GPU acceleration of probabilistic frequent itemset mining from uncertain databases (YK, TA, HK), pp. 892–901.
CIKMCIKM-2012-LiSSTDL #microblog #topic
Mining topic-level opinion influence in microblog (DL, XS, GS, JT, YD, ZL), pp. 1562–1566.
CIKMCIKM-2012-LiuCBLR #graph
Utilizing common substructures to speedup tensor factorization for mining dynamic graphs (WL, JC, JB, CL, KR), pp. 435–444.
CIKMCIKM-2012-LiuQ #generative
Mining high utility itemsets without candidate generation (ML, JFQ), pp. 55–64.
CIKMCIKM-2012-MoghaddamE #design #modelling #on the
On the design of LDA models for aspect-based opinion mining (SM, ME), pp. 803–812.
CIKMCIKM-2012-NiuRWY #effectiveness #web
An effective rule miner for instance matching in a web of data (XN, SR, HW, YY), pp. 1085–1094.
CIKMCIKM-2012-QiYZZ #multi
Mining noisy tagging from multi-label space (ZQ, MY, Z(Z, ZZ), pp. 1925–1929.
CIKMCIKM-2012-RiondatoDFU #algorithm #approximate #named #parallel #pipes and filters #random
PARMA: a parallel randomized algorithm for approximate association rules mining in MapReduce (MR, JAD, RF, EU), pp. 85–94.
CIKMCIKM-2012-TangZLW #quality #recommendation
Incorporating occupancy into frequent pattern mining for high quality pattern recommendation (LT, LZ, PL, MW), pp. 75–84.
CIKMCIKM-2012-TanLZ
Mining long-lasting exploratory user interests from search history (BT, YL, CZ), pp. 1477–1481.
CIKMCIKM-2012-WangH0 #framework #image #learning #web
A unified learning framework for auto face annotation by mining web facial images (DW, SCHH, YH), pp. 1392–1401.
CIKMCIKM-2012-WicaksonoM
Mining advices from weblogs (AFW, SHM), pp. 2347–2350.
CIKMCIKM-2012-YamamotoSIYWT #clustering #query
The wisdom of advertisers: mining subgoals via query clustering (TY, TS, MI, CY, JRW, KT), pp. 505–514.
CIKMCIKM-2012-YangTKZLDLW #learning #network
Mining competitive relationships by learning across heterogeneous networks (YY, JT, JK, YZ, JL, YD, TL, LW), pp. 1432–1441.
CIKMCIKM-2012-ZhuGCL #behaviour #query #recommendation
More than relevance: high utility query recommendation by mining users’ search behaviors (XZ, JG, XC, YL), pp. 1814–1818.
ICPRICPR-2012-DaiFZ #detection
Mining sub-categories for object detection (JD, JF, JZ), pp. 3260–3263.
ICPRICPR-2012-FuscoWY
Mining residential household information from low-resolution smart meter data (FF, MW, JWY), pp. 3545–3548.
ICPRICPR-2012-ZhangZHHT #detection #semantics
Semantic windows mining in sliding window based object detection (JZ, XZ, YH, KH, TT), pp. 3264–3267.
KDDKDD-2012-BatalFHMH #detection #multi
Mining recent temporal patterns for event detection in multivariate time series data (IB, DF, JH, FM, MH), pp. 280–288.
KDDKDD-2012-BodenGHS #graph #multi
Mining coherent subgraphs in multi-layer graphs with edge labels (BB, SG, HH, TS), pp. 1258–1266.
KDDKDD-2012-ChanLLBR #graph #multi #named
SeqiBloc: mining multi-time spanning blockmodels in dynamic graphs (JC, WL, CL, JB, KR), pp. 651–659.
KDDKDD-2012-DuivesteijnFK #distance #modelling
Different slopes for different folks: mining for exceptional regression models with cook’s distance (WD, AF, AJK), pp. 868–876.
KDDKDD-2012-FengHKBP #graph
Summarization-based mining bipartite graphs (JF, XH, BK, CB, CP), pp. 1249–1257.
KDDKDD-2012-GalbrunM #interactive #named #visualisation
Siren: an interactive tool for mining and visualizing geospatial redescriptions (EG, PM), pp. 1544–1547.
KDDKDD-2012-GuptaGSH #community #detection
Integrating community matching and outlier detection for mining evolutionary community outliers (MG, JG, YS, JH), pp. 859–867.
KDDKDD-2012-Han #network
Mining heterogeneous information networks: the next frontier (JH), pp. 2–3.
KDDKDD-2012-HendersonGETBAKFL #graph #named #scalability
RolX: structural role extraction & mining in large graphs (KH, BG, TER, HT, SB, LA, DK, CF, LL), pp. 1231–1239.
KDDKDD-2012-Holmes #data mining
Developing data mining applications (GH), p. 225.
KDDKDD-2012-LappasVG #invariant #performance
Efficient and domain-invariant competitor mining (TL, GV, DG), pp. 408–416.
KDDKDD-2012-Li12b #algorithm #graph #nondeterminism
Algorithms for mining uncertain graph data (JL), p. 813.
KDDKDD-2012-Lin #case study #data mining #experience #machine learning
Experiences and lessons in developing industry-strength machine learning and data mining software (CJL), p. 1176.
KDDKDD-2012-LiuBEWFZ #comparison #scalability
Mining large-scale, sparse GPS traces for map inference: comparison of approaches (XL, JB, JE, YW, GF, YZ), pp. 669–677.
KDDKDD-2012-LiWH
Mining event periodicity from incomplete observations (ZL, JW, JH), pp. 444–452.
KDDKDD-2012-MaoCCLKB #approach #data mining #monitoring #realtime
An integrated data mining approach to real-time clinical monitoring and deterioration warning (YM, WC, YC, CL, MK, TCB), pp. 1140–1148.
KDDKDD-2012-MatsubaraSFIY #performance
Fast mining and forecasting of complex time-stamped events (YM, YS, CF, TI, MY), pp. 271–279.
KDDKDD-2012-MukherjeeL
Mining contentions from discussions and debates (AM, BL), pp. 841–849.
KDDKDD-2012-RakthanmanonCMBWZZK #sequence
Searching and mining trillions of time series subsequences under dynamic time warping (TR, BJLC, AM, GEAPAB, MBW, QZ, JZ, EJK), pp. 262–270.
KDDKDD-2012-ShaferRBAGF #approach #monitoring #named
RainMon: an integrated approach to mining bursty timeseries monitoring data (IS, KR, VNB, YA, GRG, CF), pp. 1158–1166.
KDDKDD-2012-SondhiSTZ #framework #graph #named
SympGraph: a framework for mining clinical notes through symptom relation graphs (PS, JS, HT, CZ), pp. 1167–1175.
KDDKDD-2012-TangWYHZYGHXLU #analysis #named #topic
PatentMiner: topic-driven patent analysis and mining (JT, BW, YY, PH, YZ, XY, BG, MH, PX, WL, AKU), pp. 1366–1374.
KDDKDD-2012-TongCY #named #nondeterminism
UFIMT: an uncertain frequent itemset mining toolbox (YT, LC, PSY), pp. 1508–1511.
KDDKDD-2012-WoznicaNK #feature model #robust
Model mining for robust feature selection (AW, PN, AK), pp. 913–921.
KDDKDD-2012-WuSTY
Mining top-K high utility itemsets (CWW, BES, VST, PSY), pp. 78–86.
KDDKDD-2012-YinZC #algorithm #named #performance
USpan: an efficient algorithm for mining high utility sequential patterns (JY, ZZ, LC), pp. 660–668.
KDDKDD-2012-YuDSW #feature model #streaming
Mining emerging patterns by streaming feature selection (KY, WD, DAS, XW), pp. 60–68.
KDDKDD-2012-YuZSWWQZ #analysis #data mining #in the cloud #named #network #social
BC-PDM: data mining, social network analysis and text mining system based on cloud computing (LY, JZ, WCS, BW, BW, LQ, BRZ), pp. 1496–1499.
KDDKDD-2012-ZhangL #component #constraints #rank #recognition
Mining discriminative components with low-rank and sparsity constraints for face recognition (QZ, BL), pp. 1469–1477.
KDIRKDIR-2012-ArbelaitzGLMPP #adaptation #clustering #navigation #profiling #using
Adaptation of the User Navigation Scheme using Clustering and Frequent Pattern Mining Techiques for Profiling (OA, IG, AL, JM, JMP, IP), pp. 187–192.
KDIRKDIR-2012-ArbelaitzGLMPP12a #adaptation #web
Enhancing a Web Usage Mining based Tourism Website Adaptation with Content Information (OA, IG, AL, JM, JMP, IP), pp. 287–292.
KDIRKDIR-2012-BhattacharjeeBG #data mining #network #optimisation #using
Product Assortment Decisions for a Network of Retail Stores using Data Mining with Optimization (SB, FB, RDG), pp. 319–323.
KDIRKDIR-2012-GayLM #approach #constraints #multi #sequence
Constraint-programming Approach for Multiset and Sequence Mining (PG, BL, JM), pp. 212–220.
KEODKEOD-2012-KhairMZ #data mining #education #roadmap #student #using
Creating an Educational Roadmap for Engineering Students via an Optimal and Iterative Yearly Regression Tree using Data Mining (MK, CEM, WZ), pp. 43–52.
KMISKMIS-2012-PortelaPS #data mining #modelling #pervasive #predict
Data Mining Predictive Models for Pervasive Intelligent Decision Support in Intensive Care Medicine (FP, FP, MFS), pp. 81–88.
MLDMMLDM-2012-LiHO #approach #correlation
Top-N Minimization Approach for Indicative Correlation Change Mining (AL, MH, YO), pp. 102–116.
MLDMMLDM-2012-MacchiaCM #modelling #network #ranking
Mining Ranking Models from Dynamic Network Data (LM, MC, DM), pp. 566–577.
MLDMMLDM-2012-MondalPMMB #approach #clustering #concept analysis #using
A New Approach for Association Rule Mining and Bi-clustering Using Formal Concept Analysis (KCM, NP, AM, UM, SB), pp. 86–101.
MLDMMLDM-2012-PipanmaekapornL #effectiveness
A Pattern Discovery Model for Effective Text Mining (LP, YL), pp. 540–554.
RecSysRecSys-2012-YangCZLY #feedback #music #recommendation
Local implicit feedback mining for music recommendation (DY, TC, WZ, QL, YY), pp. 91–98.
SEKESEKE-2012-WangXC #linear #network #social
Sparse Linear Influence Model for Hot User Selection on Mining a Social Network (YW, GX, SKC), pp. 1–6.
SEKESEKE-2012-ZhangLS #graph #impact analysis
Mining Call Graph for Change Impact Analysis (QZ, BL, XS), pp. 7–12.
SIGIRSIGIR-2012-CareniniM
Methods for mining and summarizing text conversations (GC, GM), pp. 1178–1179.
SIGIRSIGIR-2012-GaoWL #graph #information retrieval #learning #scalability
Large-scale graph mining and learning for information retrieval (BG, TW, TYL), pp. 1194–1195.
SIGIRSIGIR-2012-HuQLJPZ #query #topic
Mining query subtopics from search log data (YH, YnQ, HL, DJ, JP, QZ), pp. 305–314.
SIGIRSIGIR-2012-Khatiban #trust #using
Building reputation and trust using federated search and opinion mining (SK), p. 994.
SIGIRSIGIR-2012-MoghaddamE
Aspect-based opinion mining from product reviews (SM, ME), p. 1184.
SIGIRSIGIR-2012-RaeMPB #web
Mining the web for points of interest (AR, VM, AP, HB), pp. 711–720.
SIGIRSIGIR-2012-RaveendranC #lightweight #summary
Lightweight contrastive summarization for news comment mining (GR, CLAC), pp. 1103–1104.
SIGIRSIGIR-2012-YangCZY #collaboration
Collaborative filtering with short term preferences mining (DY, TC, WZ, YY), pp. 1043–1044.
RERE-2012-Yi0ZJM #constraints #feature model #modelling
Mining binary constraints in the construction of feature models (LY, WZ, HZ, ZJ, HM), pp. 141–150.
ASEASE-2012-DingFLLZSX #online #repository
Healing online service systems via mining historical issue repositories (RD, QF, JGL, QL, DZ, JS, TX), pp. 318–321.
FSEFSE-2012-CanaveraEM #adaptation #execution
Mining the execution history of a software system to infer the best time for its adaptation (KRC, NE, SM), p. 18.
ICSEICSE-2012-Butler #identifier #java
Mining Java class identifier naming conventions (SB), pp. 1641–1643.
ICSEICSE-2012-HanDGZX #debugging #in the large #performance #stack
Performance debugging in the large via mining millions of stack traces (SH, YD, SG, DZ, TX), pp. 145–155.
ICSEICSE-2012-Hindle #power management
Green mining: Investigating power consumption across versions (AH), pp. 1301–1304.
ICSEICSE-2012-HuangLXW #recommendation #repository #xml
Mining application repository to recommend XML configuration snippets (SH, YL, YX, WW), pp. 1451–1452.
ICSEICSE-2012-Keivanloo #integration #online #repository
Online sharing and integration of results from mining software repositories (IK), pp. 1644–1646.
ICSEICSE-2012-NaganoNKAHUF #repository #scalability #using
Using the GPGPU for scaling up Mining Software Repositories (RN, HN, YK, BA, KH, NU, AF), pp. 1435–1436.
ICSEICSE-2012-PradelG #automation #debugging #detection #generative #specification #testing
Leveraging test generation and specification mining for automated bug detection without false positives (MP, TRG), pp. 288–298.
ICSEICSE-2012-SharT #injection #predict #sql
Mining input sanitization patterns for predicting SQL injection and cross site scripting vulnerabilities (LKS, HBKT), pp. 1293–1296.
ICSEICSE-2012-SunSPR #static analysis
Extending static analysis by mining project-specific rules (BS, GS, AP, BR), pp. 1054–1063.
SACSAC-2012-AccorsiS #consistency #on the #process #security
On the exploitation of process mining for security audits: the conformance checking case (RA, TS), pp. 1709–1716.
SACSAC-2012-DiamantiniPS #repository #workflow
Mining usage patterns from a repository of scientific workflows (CD, DP, ES), pp. 152–157.
SACSAC-2012-LangohrT
A model for mining relevant and non-redundant information (LL, HT), pp. 132–137.
SACSAC-2012-LeungS #constraints
A new class of constraints for constrained frequent pattern mining (CKSL, LS), pp. 199–204.
SACSAC-2012-WangTWWG #algorithm #behaviour #empirical #evaluation #process
An empirical evaluation of process mining algorithms based on structural and behavioral similarities (JW, ST, LW, RKW, QG), pp. 211–213.
SACSAC-2012-WangZHHZWT #mobile #recommendation
Context-aware role mining for mobile service recommendation (JW, CZ, CH, LH, LZ, RKW, JT), pp. 173–178.
CASECASE-2012-DunnTRR #navigation
High accuracy inertial navigation for underground mining machinery (MTD, JPT, PBR, DCR), pp. 1179–1183.
CASECASE-2012-HoSY #data mining #modelling
Data Mining of Life Log for Developing a User model-based Service Application (YH, ESS, TY), pp. 757–760.
DATEDATE-2012-MitraBD #formal method #ranking
Formal methods for ranking counterexamples through assumption mining (SM, AB, PD), pp. 911–916.
PDPPDP-2012-SaenkoK #algorithm #design #evaluation #performance #problem #search-based
Design and Performance Evaluation of Improved Genetic Algorithm for Role Mining Problem (IS, IVK), pp. 269–274.
PDPPDP-2012-SilvestriO #named
gpuDCI: Exploiting GPUs in Frequent Itemset Mining (CS, SO), pp. 416–425.
ICLPICLP-2012-BlockeelBBCP #data mining #machine learning #modelling #problem
Modeling Machine Learning and Data Mining Problems with FO(·) (HB, BB, MB, BdC, SDP, MD, AL, JR, SV), pp. 14–25.
HTHT-2011-SahW #automation #fuzzy #metadata #using
Automatic mining of cognitive metadata using fuzzy inference (MS, VW), pp. 37–46.
SIGMODSIGMOD-2011-OrdonezP #algorithm #data mining
One-pass data mining algorithms in a DBMS with UDFs (CO, SKP), pp. 1217–1220.
SIGMODSIGMOD-2011-PoundPT #approach #web
Facet discovery for structured web search: a query-log mining approach (JP, SP, PT), pp. 169–180.
SIGMODSIGMOD-2011-ZhangZD #corpus #estimation #performance
Mining a search engine’s corpus: efficient yet unbiased sampling and aggregate estimation (MZ, NZ, GD), pp. 793–804.
TPDLTPDL-2011-KnothRZ #repository #semantics #using
Connecting Repositories in the Open Access Domain Using Text Mining and Semantic Data (PK, VR, ZZ), pp. 483–487.
TPDLTPDL-2011-TsatsaronisVTRNSZ #graph #how #modelling
How to Become a Group Leader? or Modeling Author Types Based on Graph Mining (GT, IV, ST, MR, KN, MS, MZ), pp. 15–26.
VLDBVLDB-2011-YangPS #graph #multi #performance
Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining (XY, SP, PS), pp. 231–242.
VLDBVLDB-2011-ZhuQLYHY #network #scalability
Mining Top-K Large Structural Patterns in a Massive Network (FZ, QQ, DL, XY, JH, PSY), pp. 807–818.
VLDBVLDB-2012-BarskyKWH11 #correlation #dataset #scalability #taxonomy
Mining Flipping Correlations from Large Datasets with Taxonomies (MB, SK, TW, JH), pp. 370–381.
VLDBVLDB-2012-LiuZW11
Controlling False Positives in Association Rule Mining (GL, HZ, LW), pp. 145–156.
CSMRCSMR-2011-BelderrarKGAG #architecture #evolution #identification #object-oriented
Sub-graph Mining: Identifying Micro-architectures in Evolving Object-Oriented Software (AB, SK, YGG, GA, PG), pp. 171–180.
CSMRCSMR-2011-LamkanfiDSV #algorithm #debugging #predict
Comparing Mining Algorithms for Predicting the Severity of a Reported Bug (AL, SD, QDS, TV), pp. 249–258.
CSMRCSMR-2011-PoncinSB #process #repository
Process Mining Software Repositories (WP, AS, MvdB), pp. 5–14.
ICPCICPC-2011-Medini #automation #concept #execution #scalability
Scalable Automatic Concept Mining from Execution Traces (SM), pp. 238–241.
ICSMEICSM-2011-ButlerWYS #java
Mining java class naming conventions (SB, MW, YY, HS), pp. 93–102.
ICSMEICSM-2011-DAmbrosR #effectiveness #repository
Effective mining of software repositories (MD, RR), p. 598.
ICSMEICSM-2011-PirzadehHS #analysis #execution
Exploiting text mining techniques in the analysis of execution traces (HP, AHL, MS), pp. 223–232.
ICSMEICSM-2011-TarvoZC #algorithm #branch #integration #multi #version control
An integration resolution algorithm for mining multiple branches in version control systems (AT, TZ, JC), pp. 402–411.
ICSMEICSM-2011-YousefiS #distributed #identification
Identifying distributed features in SOA by mining dynamic call trees (AY, KS), pp. 73–82.
MSRMSR-2011-Whitehead #data mining #game studies #what
Fantasy, farms, and freemium: what game data mining teaches us about retention, conversion, and virality (JW), p. 1.
WCREWCRE-2011-NadiH #linux
Make it or Break it: Mining Anomalies from Linux Kbuild (SN, RCH), pp. 315–324.
CoGCIG-2011-AsheSK #data mining #machine learning #named
Keynotes: Data mining and machine learning applications in MMOs (GA, NRS, JHK).
CHICHI-2011-LeeKF #behaviour #design #persuasion
Mining behavioral economics to design persuasive technology for healthy choices (MKL, SBK, JF), pp. 325–334.
HCIHCI-UA-2011-LinHLYH #using
Using Grounded Theory and Text Mining to Find Interesting Reading Materials for Slow EFL Learners (YCL, ClH, MHL, HFY, CFH), pp. 77–85.
HCIHIMI-v2-2011-MozaffariM #classification #visual notation
A Classification Scheme for Characterizing Visual Mining (EM, SPM), pp. 46–54.
HCIOCSC-2011-GaoQZ #social
Mining Social Relationships in Micro-blogging Systems (QG, QQ, XZ), pp. 110–119.
CAiSECAiSE-2011-BoseAZP #concept #process
Handling Concept Drift in Process Mining (RPJCB, WMPvdA, IZ, MP), pp. 391–405.
ICEISICEIS-J-2011-WangN11a #named #nondeterminism
UF-Evolve: Uncertain Frequent Pattern Mining (SW, VTYN), pp. 98–116.
ICEISICEIS-v1-2011-Cuzzocrea #approach #information management
A UML-extended Approach for Mining OLAP Data Cubes in Complex Knowledge Discovery Environments (AC), pp. 281–289.
ICEISICEIS-v1-2011-SantosP #data mining #preprocessor #ubiquitous
Enabling Ubiquitous Data Mining in Intensive Care — Features Selection and Data Pre-processing (MS, FP), pp. 261–266.
ICEISICEIS-v1-2011-ThitiprayoonwongseSS #data mining
Data Mining on Dengue Virus Disease (DT, PS, NS), pp. 32–41.
ICEISICEIS-v1-2011-WangN #named #nondeterminism
UF-Evolve — Uncertain Frequent Pattern Mining (SW, VTYN), pp. 74–84.
ICEISICEIS-v4-2011-Naderipour #ad hoc #industrial #process #using
Mining of Ad-hoc Business Processes using Microsoft Sharepoint, Nitro & Prom 6.0 — An Industrial Practice (FN), pp. 413–418.
CIKMCIKM-2011-DongZCZZLWO #database #named #performance
e-NSP: efficient negative sequential pattern mining based on identified positive patterns without database rescanning (XD, ZZ, LC, YZ, CZ, JL, WW, YO), pp. 825–830.
CIKMCIKM-2011-GuoZTG #data type #multi
Mining frequent patterns across multiple data streams (JG, PZ, JT, LG), pp. 2325–2328.
CIKMCIKM-2011-KimJHSZ #approach #corpus #graph
Mining entity translations from comparable corpora: a holistic graph mapping approach (JK, LJ, SwH, YIS, MZ), pp. 1295–1304.
CIKMCIKM-2011-KiselevaAB #case study #query
Mining query structure from click data: a case study of product queries (JK, EA, DB), pp. 2217–2220.
CIKMCIKM-2011-KrishnaswamyGG #data type #mobile #roadmap #ubiquitous
Advances in data stream mining for mobile and ubiquitous environments (SK, JG, MMG), pp. 2607–2608.
CIKMCIKM-2011-LoSZL #community #network #trust
Mining direct antagonistic communities in explicit trust networks (DL, DS, KZ, EPL), pp. 1013–1018.
CIKMCIKM-2011-OrdonezG #data mining #database #query #relational #sql
A data mining system based on SQL queries and UDFs for relational databases (CO, CGA), pp. 2521–2524.
CIKMCIKM-2011-XuJL #image #online #ranking
Retrieving and ranking unannotated images through collaboratively mining online search results (SX, HJ, FCML), pp. 485–494.
CIKMCIKM-2011-YeungJ #how #scalability #towards
Studying how the past is remembered: towards computational history through large scale text mining (CmAY, AJ), pp. 1231–1240.
CIKMCIKM-2011-ZhangJJ #framework
Promotional subspace mining with EProbe framework (YZ, YJ, WJ), pp. 2185–2188.
ECIRECIR-2011-Carrillo-de-AlbornozPGD #analysis #overview #rating #sentiment
A Joint Model of Feature Mining and Sentiment Analysis for Product Review Rating (JCdA, LP, PG, AD), pp. 55–66.
KDDKDD-2011-BatistaKMR #data mining
SIGKDD demo: sensors and software to allow computational entomology, an emerging application of data mining (GEAPAB, EJK, AMN, ER), pp. 761–764.
KDDKDD-2011-Bie #data mining #framework
An information theoretic framework for data mining (TDB), pp. 564–572.
KDDKDD-2011-BifetHPG #data type #evolution #graph
Mining frequent closed graphs on evolving data streams (AB, GH, BP, RG), pp. 591–599.
KDDKDD-2011-Boire #case study #data mining #lessons learnt
The practitioner’s viewpoint to data mining: key lessons learned in the trenches and case studies (RB), p. 785.
KDDKDD-2011-Elder
Thriving as a data miner in the real world (JFEI), p. 786.
KDDKDD-2011-GhotingKPK #algorithm #data mining #implementation #machine learning #named #parallel #pipes and filters #tool support
NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce (AG, PK, EPDP, RK), pp. 334–342.
KDDKDD-2011-GoethalsMV #framework #interactive #named #visual notation
MIME: a framework for interactive visual pattern mining (BG, SM, JV), pp. 757–760.
KDDKDD-2011-HendersonGLAETF #graph #recursion #using
It’s who you know: graph mining using recursive structural features (KH, BG, LL, LA, TER, HT, CF), pp. 663–671.
KDDKDD-2011-Inchiosa #data mining #scalability #using
Accelerating large-scale data mining using in-database analytics (MEI), p. 778.
KDDKDD-2011-JinWLYH #named #network #power of #social #social media
LikeMiner: a system for mining the power of “like” in social media networks (XJ, CW, JL, XY, JH), pp. 753–756.
KDDKDD-2011-KaufmanRP #data mining #detection
Leakage in data mining: formulation, detection, and avoidance (SK, SR, CP), pp. 556–563.
KDDKDD-2011-LathiaC
Mining mobility data to minimise travellers’ spending on public transport (NL, LC), pp. 1181–1189.
KDDKDD-2011-McCloskeyKIKB #behaviour #data mining #using
From market baskets to mole rats: using data mining techniques to analyze RFID data describing laboratory animal behavior (DPM, MEK, SPI, IK, SBM), pp. 301–306.
KDDKDD-2011-MohammedCFY #data mining
Differentially private data release for data mining (NM, RC, BCMF, PSY), pp. 493–501.
KDDKDD-2011-MukerjeePG #algorithm #framework #linear #platform #semantics #sql
Linear scale semantic mining algorithms in microsoft SQL server’s semantic platform (KM, TP, SG), pp. 213–221.
KDDKDD-2011-PatnaikBRPKH #case study #challenge #experience #sequence
Experiences with mining temporal event sequences from electronic medical records: initial successes and some challenges (DP, PB, NR, LP, BJK, DAH), pp. 360–368.
KDDKDD-2011-QiYZZ #image
Mining partially annotated images (ZQ, MY, Z(Z, ZZ), pp. 1199–1207.
KDDKDD-2011-RamageMD #modelling #topic
Partially labeled topic models for interpretable text mining (DR, CDM, STD), pp. 457–465.
KDDKDD-2011-Rejto #data mining #information management #research
Knowledge discovery and data mining in pharmaceutical cancer research (PAR), p. 781.
KDDKDD-2011-TattiC
Mining closed episodes with simultaneous events (NT, BC), pp. 1172–1180.
KDDKDD-2011-TrasartiPNG
Mining mobility user profiles for car pooling (RT, FP, MN, FG), pp. 1190–1198.
KDDKDD-2011-VijayaraghavanK #data mining #machine learning #online
Applications of data mining and machine learning in online customer care (RV, PVK), p. 779.
KDDKDD-2011-ZhengSTLLC #challenge #data mining #information management #mobile
Applying data mining techniques to address disaster information management challenges on mobile devices (LZ, CS, LT, TL, SL, SCC), pp. 283–291.
KDIRKDIR-2011-AlouaouiTF #query
Querying and Mining Spatiotemporal Association Rules (HA, SYT, SF), pp. 402–405.
KDIRKDIR-2011-BonninBB #predict #web
Handling Tabbing and Backward References for Predictive Web Usage Mining (GB, AB, AB), pp. 503–509.
KDIRKDIR-2011-BressoBSMKSND #reduction #using
Use of Domain Knowledge for Dimension Reduction — Application to Mining of Drug Side Effects (EB, SB, MST, GM, ASK, MS, AN, MDD), pp. 271–276.
KDIRKDIR-2011-DAcuntoBDLPPS #3d #re-engineering
Inferential Mining for Reconstruction of 3D Cell Structures in Atomic Force Microscopy Imaging (MD, SB, SD, ML, MP, AP, OS), pp. 348–353.
KDIRKDIR-2011-HsiehY #case study
A Study of Profit Mining (YLH, DLY), pp. 510–514.
KDIRKDIR-2011-MukerjeePG #database #documentation #relational #semantics
Semantic Mining of Documents in a Relational Database (KM, TP, SG), pp. 146–158.
KDIRKDIR-2011-OmerBG #algorithm #motivation #using
A New Frequent Similar Tree Algorithm Motivated by Dom Mining — Using RTDM and its New Variant — SiSTeR (OB, RB, SG), pp. 238–243.
KDIRKDIR-2011-Vanetik #algorithm #graph #performance
A Fast Algorithm for Mining Graphs of Prescribed Connectivity (NV), pp. 5–13.
KDIRKDIR-2011-VanetikG #named
HashMax: A New Method for Mining Maximal Frequent Itemsets (NV, EG), pp. 140–145.
MLDMMLDM-2011-ChenER #parametricity #using
Mining Spatial Trajectories Using Non-parametric Density Functions (CSC, CFE, NJR), pp. 496–510.
MLDMMLDM-2011-Kessl #parallel #using
Static Load Balancing of Parallel Mining of Frequent Itemsets Using Reservoir Sampling (RK), pp. 553–567.
MLDMMLDM-2011-SimeonH #approach #correlation #difference #named #set
GENCCS: A Correlated Group Difference Approach to Contrast Set Mining (MS, RJH), pp. 140–154.
MLDMMLDM-2011-TalbertHT #data mining #framework #machine learning
A Machine Learning and Data Mining Framework to Enable Evolutionary Improvement in Trauma Triage (DAT, MH, ST), pp. 348–361.
SEKESEKE-2011-CellierDFR #data mining #fault #locality #multi
Multiple Fault Localization with Data Mining (PC, MD, SF, OR), pp. 238–243.
SEKESEKE-2011-LemosSLO #consistency #development #process
Conformance Checking of Software Development Processes Through Process Mining (AML, CCS, RMFL, CALO), pp. 654–659.
SEKESEKE-2011-WuXKP #analysis #data mining #debugging #named #reliability
BUGMINER: Software Reliability Analysis Via Data Mining of Bug Reports (LW, BX, GEK, RJP), pp. 95–100.
SIGIRSIGIR-2011-JiangPL #web
Enhancing web search by mining search and browse logs (DJ, JP, HL), pp. 1295–1296.
SIGIRSIGIR-2011-KimKWHK #approach #classification
Authorship classification: a discriminative syntactic tree mining approach (SK, HK, TW, JH, HDK), pp. 455–464.
SIGIRSIGIR-2011-LappasPS #network #social #using
Mining tags using social endorsement networks (TL, KP, TS), pp. 195–204.
SIGIRSIGIR-2011-WangHH #image #search-based #web
Mining weakly labeled web facial images for search-based face annotation (DW, SCHH, YH), pp. 535–544.
SIGIRSIGIR-2011-ZhengGYXBSHY #community #topic
Mining topics on participations for community discovery (GZ, JG, LY, SX, SB, ZS, DH, YY), pp. 445–454.
ECOOPECOOP-2011-MilevaWZ #evolution
Mining Evolution of Object Usage (YMM, AW, AZ), pp. 105–129.
PLDIPLDI-2011-DEliaDF
Mining hot calling contexts in small space (DCD, CD, IF), pp. 516–527.
REFSQREFSQ-2011-GervasiZ #requirements
Mining Requirements Links (VG, DZ), pp. 196–201.
ASEASE-2011-WuLWXM #specification
Iterative mining of resource-releasing specifications (QW, GL, QW, TX, HM), pp. 233–242.
ASEASE-2011-ZhengMLXK #testing #web
Mining test oracles of web search engines (WZ, HM, MRL, TX, IK), pp. 408–411.
ESEC-FSEESEC-FSE-2011-Bettenburg #collaboration #development #repository
Mining development repositories to study the impact of collaboration on software systems (NB), pp. 376–379.
ESEC-FSEESEC-FSE-2011-Schur #enterprise #specification
Experimental specification mining for enterprise applications (MS), pp. 388–391.
ICSEICSE-2011-AthanasopoulosZVI #abstraction
Mining service abstractions (DA, AZ, PV, VI), pp. 944–947.
ICSEICSE-2011-DumitruGHCMCM #on-demand #recommendation
On-demand feature recommendations derived from mining public product descriptions (HD, MG, NH, JCH, BM, CCH, MM), pp. 181–190.
ICSEICSE-2011-Kumar #concurrent #distributed #specification
Specification mining in concurrent and distributed systems (SK), pp. 1086–1089.
ICSEICSE-2011-Kumar11a #concurrent #distributed #specification
Specification mining in concurrent and distributed systems (SK), pp. 1161–1163.
ICSEICSE-2011-KumarKRL #graph #sequence
Mining message sequence graphs (SK, SCK, AR, DL), pp. 91–100.
ICSEICSE-2011-LeeCR #parametricity #specification
Mining parametric specifications (CL, FC, GR), pp. 591–600.
ICSEICSE-2011-PortNHH #case study #development #experience #scalability
Experiences with text mining large collections of unstructured systems development artifacts at jpl (DP, APN, JH, LH), pp. 701–710.
ICSEICSE-2011-TanZP #concurrent #debugging #detection #named
aComment: mining annotations from comments and code to detect interrupt related concurrency bugs (LT, YZ, YP), pp. 11–20.
ICSEICSE-2011-Thomas #modelling #repository #topic #using
Mining software repositories using topic models (SWT), pp. 1138–1139.
SACSAC-2011-AhluwaliaGCY #privacy
Target-based privacy preserving association rule mining (MA, AG, ZC, YY), pp. 962–967.
SACSAC-2011-BanZATK
Mining interlacing manifolds in high dimensional spaces (TB, CZ, SA, TT, YK), pp. 942–949.
SACSAC-2011-CuzzocreaS #clustering #database #framework #named
ClustCube: an OLAP-based framework for clustering and mining complex database objects (AC, PS), pp. 976–982.
SACSAC-2011-Fournier-VigerNT #named #sequence
RuleGrowth: mining sequential rules common to several sequences by pattern-growth (PFV, RN, VSMT), pp. 956–961.
SACSAC-2011-KadriE #nondeterminism #sequence #web
Mining uncertain web log sequences with access history probabilities (OK, CIE), pp. 1059–1060.
SACSAC-2011-LeungJ #data type #nondeterminism #using
Frequent itemset mining of uncertain data streams using the damped window model (CKSL, FJ), pp. 950–955.
SACSAC-2011-LeungS #equivalence #nondeterminism
Equivalence class transformation based mining of frequent itemsets from uncertain data (CKSL, LS), pp. 983–984.
SACSAC-2011-Perez-CastilloGPWP #comparison #empirical #process
An empirical comparison of static and dynamic business process mining (RPC, IGRdG, MP, BW, ÁSP), pp. 272–279.
CASECASE-2011-JamesBHACC #mobile #using
Tele-operation of a mobile mining robot using a panoramic display: an exploration of operators sense of presence (CAJ, TPB, KH, LA, CC, AC), pp. 279–284.
HPDCHPDC-2011-BalkirFR #architecture #distributed #pipes and filters #using
A distributed look-up architecture for text mining applications using mapreduce (ASB, ITF, AR), pp. 279–280.
PDPPDP-2011-CesarioT #data mining #distributed #framework #grid
A Failure Handling Framework for Distributed Data Mining Services on the Grid (EC, DT), pp. 70–79.
PDPPDP-2011-SaenkoK #algorithm #problem #search-based
Genetic Algorithms for Role Mining Problem (IS, IVK), pp. 646–650.
ECSAECSA-2010-KamalA #design pattern
Mining Relationships between the Participants of Architectural Patterns (AWK, PA), pp. 401–408.
SIGMODSIGMOD-2010-HanSYY #analysis #approach #database #network
Mining knowledge from databases: an information network analysis approach (JH, YS, XY, PSY), pp. 1251–1252.
SIGMODSIGMOD-2010-KhanYW #graph #proximity #scalability #towards
Towards proximity pattern mining in large graphs (AK, XY, KLW), pp. 867–878.
SIGMODSIGMOD-2010-LiJLTYHK #database #named
MoveMine: mining moving object databases (ZL, MJ, JGL, LAT, YY, JH, RK), pp. 1203–1206.
SIGMODSIGMOD-2010-OrdonezG #data mining #database #research
Database systems research on data mining (CO, JGG), pp. 1253–1254.
SIGMODSIGMOD-2010-ParameswaranKBG #algorithm #named #precedence #recommendation
Recsplorer: recommendation algorithms based on precedence mining (AGP, GK, BB, HGM), pp. 87–98.
VLDBVLDB-2010-AggarwalLYJ #graph #on the
On Dense Pattern Mining in Graph Streams (CCA, YL, PSY, RJ), pp. 975–984.
VLDBVLDB-2010-BedathurBDMW #ad hoc
Interesting-Phrase Mining for Ad-Hoc Text Analytics (SJB, KB, JD, NM, GW), pp. 1348–1357.
VLDBVLDB-2010-CaoCJ10a #semantics
Mining Significant Semantic Locations From GPS Data (XC, GC, CSJ), pp. 1009–1020.
VLDBVLDB-2010-LiDHK #clustering #named
Swarm: Mining Relaxed Temporal Moving Object Clusters (ZL, BD, JH, RK), pp. 723–734.
VLDBVLDB-2010-Muthukrishnan #data transformation #internet
Data Management and Mining in Internet Ad Systems (SM), pp. 1655–1656.
VLDBVLDB-2010-RenzCK #database #nondeterminism #similarity
Similarity Search and Mining in Uncertain Databases (MR, RC, HPK, AZ, TB), pp. 1653–1654.
EDMEDM-2010-BernauerP #data mining #student
Data Mining of both Right and Wrong Answers from a Mathematics and a Science M/C Test given Collectively to 11, 228 Students from India in years 4, 6 and 8 (JB, JP), pp. 273–274.
EDMEDM-2010-DMelloG #experience #learning
Mining Bodily Patterns of Affective Experience during Learning (SKD, ACG), pp. 31–40.
EDMEDM-2010-DominguezYC #data mining #generative #python
Data Mining for Generating Hints in a Python Tutor (AKD, KY, JRC), pp. 91–100.
EDMEDM-2010-FalakmasirH #data mining #education #using
Using Educational Data Mining Methods to Study the Impact of Virtual Classroom in E-Learning (MHF, JH), pp. 241–248.
EDMEDM-2010-HowardJN #process #using
Examining Learner Control in a Structured Inquiry Cycle Using Process Mining (LH, JJ, CN), pp. 71–80.
EDMEDM-2010-KrugerMW #analysis #education
A Data Model to Ease Analysis and Mining of Educational Data (AK, AM, BW), pp. 131–140.
EDMEDM-2010-KrugerMW10a #data analysis #data mining
When Data Exploration and Data Mining meet while Analysing Usage Data of a Course (AK, AM, BW), pp. 305–306.
EDMEDM-2010-Rajibussalim #interactive #learning #student
Mining Students’ Interaction Data from a System that Support Learning by Reflection (R), pp. 249–256.
EDMEDM-2010-RomeroRLV
Mining Rare Association Rules from e-Learning Data (CR, JRR, JML, SV), pp. 171–180.
EDMEDM-2010-RomeroVVP #student #testing
Class Association Rules Mining from Students’ Test Data (CR, SV, EV, MP), pp. 317–318.
EDMEDM-2010-SouthavilayYC #collaboration #process #student
Process Mining to Support Students’ Collaborative Writing (VS, KY, RAC), pp. 257–266.
EDMEDM-2010-SrinivasBG
Mining information from tutor data to improve pedagogical content knowledge (SS, MB, AG), pp. 275–276.
EDMEDM-2010-VialardiCBVEPO #case study #data mining #student
A Case Study: Data Mining Applied to Student Enrollment (CVS, JC, AB, DV, JE, JPP, AO), pp. 333–334.
EDMEDM-2010-XiongLS #locality #performance #problem
Assessing Reviewer’s Performance Based on Mining Problem Localization in Peer-Review Data (WX, DJL, CDS), pp. 211–220.
ICPCICPC-2010-RevelleDP #data fusion #feature model #using #web
Using Data Fusion and Web Mining to Support Feature Location in Software (MR, BD, DP), pp. 14–23.
MSRMSR-2010-BruchMM #framework #reuse #subclass
Mining subclassing directives to improve framework reuse (MB, MM, MM), pp. 141–150.
MSRMSR-2010-GegickRX #case study #debugging #identification #industrial #security
Identifying security bug reports via text mining: An industrial case study (MG, PR, TX), pp. 11–20.
MSRMSR-2010-Herbsleb #named #question
MSR: Mining for scientific results? (JDH).
MSRMSR-2010-HindleHSJ #challenge #gnome
Mining Challenge 2010: FreeBSD, GNOME Desktop and Debian/Ubuntu (AH, IH, ES, ZMJ), pp. 82–85.
MSRMSR-2010-MauczkaSFBG #security
Mining security changes in FreeBSD (AM, CS, FF, MB, TG), pp. 90–93.
MSRMSR-2010-NagappanV
Abstracting log lines to log event types for mining software system logs (MN, MAV), pp. 114–117.
MSRMSR-2010-NussbaumZ #assurance #data mining #database #metadata #quality
The Ultimate Debian Database: Consolidating bazaar metadata for Quality Assurance and data mining (LN, SZ), pp. 52–61.
MSRMSR-2010-PosnettBD #java #named
THEX: Mining metapatterns from java (DP, CB, PTD), pp. 122–125.
MSRMSR-2010-Robles #case study #repository
Replicating MSR: A study of the potential replicability of papers published in the Mining Software Repositories proceedings (GR), pp. 171–180.
MSRMSR-2010-VetroTM #java #precise
Assessing the precision of FindBugs by mining Java projects developed at a university (AV, MT, MM), pp. 110–113.
SCAMSCAM-2010-Zeller #in the large #learning #modelling
Learning from 6,000 Projects: Mining Models in the Large (AZ), pp. 3–6.
WCREWCRE-2010-LozanoKMA #source code
Mining Source Code for Structural Regularities (AL, AK, KM, GA), pp. 22–31.
WCREWCRE-2010-SurianLL #collaboration #developer #network #scalability
Mining Collaboration Patterns from a Large Developer Network (DS, DL, EPL), pp. 269–273.
FDGFDG-2010-LewisW #case study #game studies #statistics #web #web service
Mining game statistics from web services: a World of Warcraft armory case study (CL0, NWF), pp. 100–107.
CSCWCSCW-2010-GuyJPRU #people #similarity #social #social media
Same places, same things, same people?: mining user similarity on social media (IG, MJ, AP, IR, EU), pp. 41–50.
CAiSECAiSE-2010-AalstPS #process
Beyond Process Mining: From the Past to Present and Future (WMPvdA, MP, MS), pp. 38–52.
EDOCEDOC-2010-KalsingNIT #approach #incremental #legacy #process
An Incremental Process Mining Approach to Extract Knowledge from Legacy Systems (AK, GSdN, CI, LHT), pp. 79–88.
ICEISICEIS-AIDSS-2010-BenayadiG #framework #sequence
Mining Timed Sequences with TOM4L Framework (NB, MLG), pp. 111–120.
ICEISICEIS-AIDSS-2010-CarvalhoSP #data mining
Swarm Intelligence for Rule Discovery in Data Mining (ABdC, TS, AP), pp. 314–319.
ICEISICEIS-AIDSS-2010-CuzzocreaFP #abstraction #analysis #collaboration #effectiveness #flexibility #process
Effective Analysis of Flexible Collaboration Processes by Way of Abstraction and Mining Techniques (AC, FF, LP), pp. 157–166.
ICEISICEIS-AIDSS-2010-Vilas-BoasSPSR #data mining #predict
Hourly Prediction of Organ Failure and Outcome in Intensive Care based on Data Mining Techniques (MVB, MFS, FP, ÁMS, FR), pp. 270–277.
ICEISICEIS-DISI-2010-KalsingTI #algorithm #incremental #process
An Incremental Process Mining Algorithm (AK, LHT, CI), pp. 263–268.
ICEISICEIS-DISI-2010-ZhangOCK #case study #data mining #education #student
Use Data Mining to Improve Student Retention in Higher Education — A Case Study (YZ, SO, TC, HK), pp. 190–197.
ICEISICEIS-ISAS-2010-EliceguiVM #data mining #semantics
Combining Semantic Technologies and Data Mining to Endow BSS/OSS Systems with Intelligence — Particularization to an International Telecom Company Tariff System (JME, GTdV, MdFM), pp. 350–355.
ICEISICEIS-ISAS-2010-KikuchiMAM #process
Process Mining for Job Nets in Integrated Complex Computer Systems (SK, YM, MA, SM), pp. 5–12.
ICEISICEIS-J-2010-KikuchiMAM10a #enterprise #process
Process Mining for Job Nets in Integrated Enterprise Systems (SK, YM, MA, SM), pp. 299–310.
CIKMCIKM-2010-BingSJZL #documentation #learning #ontology #representation
Learning ontology resolution for document representation and its applications in text mining (LB, BS, SJ, YZ, WL), pp. 1713–1716.
CIKMCIKM-2010-CaoBYCT #approach #effectiveness #mobile
An effective approach for mining mobile user habits (HC, TB, QY, EC, JT), pp. 1677–1680.
CIKMCIKM-2010-ChenJPL #algorithm #database #performance #scalability
An efficient algorithm for mining time interval-based patterns in large database (YCC, JCJ, WCP, SYL), pp. 49–58.
CIKMCIKM-2010-FlorezD #process
Mining rules to explain activities in videos (OUF, CED), pp. 1577–1580.
CIKMCIKM-2010-GaoYW #multi #named #query
SEQUEL: query completion via pattern mining on multi-column structural data (CG, QY, JW), pp. 1955–1956.
CIKMCIKM-2010-GodboleBGV #repository #taxonomy
Building re-usable dictionary repositories for real-world text mining (SG, IB, AG, AV), pp. 1189–1198.
CIKMCIKM-2010-GuoZGZS #analysis #named
OpinionIt: a text mining system for cross-lingual opinion analysis (HG, HZ, ZG, XZ, ZS), pp. 1199–1208.
CIKMCIKM-2010-LeungLLW #network #social
Mining interesting link formation rules in social networks (CWkL, EPL, DL, JW), pp. 209–218.
CIKMCIKM-2010-LiuTHJY #network #topic
Mining topic-level influence in heterogeneous networks (LL, JT, JH, MJ, SY), pp. 199–208.
CIKMCIKM-2010-MoghaddamE
Opinion digger: an unsupervised opinion miner from unstructured product reviews (SM, ME), pp. 1825–1828.
CIKMCIKM-2010-SeseSF #network
Mining networks with shared items (JS, MS, MF), pp. 1681–1684.
CIKMCIKM-2010-WangCLC #approach #modelling #probability
Accelerating probabilistic frequent itemset mining: a model-based approach (LW, RC, SDL, DWLC), pp. 429–438.
CIKMCIKM-2010-WangJL #collaboration #process
Collaboration analytics: mining work patterns from collaboration activities (QW, HJ, YL), pp. 1861–1864.
CIKMCIKM-2010-ZhangSZBA #documentation #named #parallel #probability #topic
PTM: probabilistic topic mapping model for mining parallel document collections (DZ, JS, CZ, AB, NA), pp. 1653–1656.
CIKMCIKM-2010-ZhouLBXL #information management #reasoning #set
Rough sets based reasoning and pattern mining for a two-stage information filtering system (XZ, YL, PB, YX, RYKL), pp. 1429–1432.
CIKMCIKM-2010-ZhuangLSHXSX #classification #collaboration #multi
Collaborative Dual-PLSA: mining distinction and commonality across multiple domains for text classification (FZ, PL, ZS, QH, YX, ZS, HX), pp. 359–368.
ECIRECIR-2010-DaiD #retrieval #roadmap
Mining Anchor Text Trends for Retrieval (ND, BDD), pp. 127–139.
ECIRECIR-2010-DaiDW #topic
Mining Neighbors’ Topicality to Better Control Authority Flow (ND, BDD, YW), pp. 653–657.
ECIRECIR-2010-Shi #information retrieval #web
Mining OOV Translations from Mixed-Language Web Pages for Cross Language Information Retrieval (LS), pp. 471–482.
ICMLICML-2010-DasguptaN #clustering
Mining Clustering Dimensions (SD, VN), pp. 263–270.
ICMLICML-2010-XuHFPJ #detection #problem #scalability
Detecting Large-Scale System Problems by Mining Console Logs (WX, LH, AF, DAP, MIJ), pp. 37–46.
ICPRICPR-2010-Artigas-FuentesGB #approximate #similarity
A High-Dimensional Access Method for Approximated Similarity Search in Text Mining (FJAF, RGG, JMBC), pp. 3155–3158.
ICPRICPR-2010-XiaLH #modelling #using
Mining Exemplars for Object Modelling Using Affinity Propagation (SX, JL, ERH), pp. 918–921.
KDDKDD-2010-Feldman #data mining #lessons learnt #quantifier #scalability
The quantification of advertising: (+ lessons from building businesses based on large scale data mining) (KF), pp. 5–6.
KDDKDD-2010-FriedmanS #data mining #difference #privacy
Data mining with differential privacy (AF, AS), pp. 493–502.
KDDKDD-2010-GaoW #nondeterminism
Direct mining of discriminative patterns for classifying uncertain data (CG, JW), pp. 861–870.
KDDKDD-2010-HendersonEFALMPT #approach #forensics #graph #metric #multi
Metric forensics: a multi-level approach for mining volatile graphs (KH, TER, CF, LA, LL, KM, BAP, HT), pp. 163–172.
KDDKDD-2010-KarguptaGF #data mining #generative
The next generation of transportation systems, greenhouse emissions, and data mining (HK, JG, WF), pp. 1209–1212.
KDDKDD-2010-KarguptaSG #data mining #distributed #overview #performance
MineFleet®: an overview of a widely adopted distributed vehicle performance data mining system (HK, KS, MG), pp. 37–46.
KDDKDD-2010-KumarGM #data mining #fault #health #predict
Data mining to predict and prevent errors in health insurance claims processing (MK, RG, ZSM), pp. 65–74.
KDDKDD-2010-LamC #data type #flexibility
Mining top-k frequent items in a data stream with flexible sliding windows (HTL, TC), pp. 283–292.
KDDKDD-2010-LiAZ
Mining positive and negative patterns for relevance feature discovery (YL, AA, NZ), pp. 753–762.
KDDKDD-2010-LiDHKN #behaviour
Mining periodic behaviors for moving objects (ZL, BD, JH, RK, PN), pp. 1099–1108.
KDDKDD-2010-LouFYLW #workflow
Mining program workflow from interleaved traces (JGL, QF, SY, JL, BW), pp. 613–622.
KDDKDD-2010-Lu #data mining #industrial #online
Data mining in the online services industry (QL), pp. 1–2.
KDDKDD-2010-MaxwellBR #graph #memory management #using
Diagnosing memory leaks using graph mining on heap dumps (EKM, GB, NR), pp. 115–124.
KDDKDD-2010-Ruggieri
Frequent regular itemset mining (SR), pp. 263–272.
KDDKDD-2010-SunCCC #nondeterminism #probability
Mining uncertain data with probabilistic guarantees (LS, RC, DWC, JC), pp. 273–282.
KDDKDD-2010-TaiYC #outsourcing #pseudo #taxonomy
k-Support anonymity based on pseudo taxonomy for outsourcing of frequent itemset mining (CHT, PSY, MSC), pp. 473–482.
KDDKDD-2010-TsengWSY #algorithm #named #performance
UP-Growth: an efficient algorithm for high utility itemset mining (VST, CWW, BES, PSY), pp. 253–262.
KDDKDD-2010-WangCSX #algorithm #mobile #network #social
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks (YW, GC, GS, KX), pp. 1039–1048.
KDDKDD-2010-WangHJTZYG #network #research
Mining advisor-advisee relationships from research publication networks (CW, JH, YJ, JT, DZ, YY, JG), pp. 203–212.
KDDKDD-2010-YangNSS #data mining #privacy
Collusion-resistant privacy-preserving data mining (BY, HN, IS, JS), pp. 483–492.
KDDKDD-2010-ZhengSTLLCH #data mining #network #using
Using data mining techniques to address critical information exchange needs in disaster affected public-private networks (LZ, CS, LT, TL, SL, SCC, VH), pp. 125–134.
KDIRKDIR-2010-AchaKV #analysis #data mining #modelling
Tactical Analysis Modeling through Data Mining — Pattern Discovery in Racket Sports (ATA, WAK, JKV), pp. 176–181.
KDIRKDIR-2010-CarulloB #analysis #machine learning #web
Machine Learning and Link Analysis for Web Content Mining (MC, EB), pp. 156–161.
KDIRKDIR-2010-CorreiaCL #architecture #collaboration #data mining
An Architecture for Collaborative Data Mining (FC, RC, JCL), pp. 467–470.
KDIRKDIR-2010-Ghosh #locality #modelling #multi #predict #scalability #using
Actionable Mining of Large, Multi-relational Data using Localized Predictive Models (JG), pp. 9–10.
KDIRKDIR-2010-Rauch #data mining #logic #semantics #web
Logic of Discovery, Data Mining and Semantic Web — Position Paper (JR), pp. 342–351.
KDIRKDIR-2010-Seifert #knowledge-based #music
Knowledge-based Mining of Patterns and Structure of Symbolic Music Files (FS0), pp. 358–363.
KDIRKDIR-2010-Vazquez-RodriguezPGFC #data mining #visual notation
A New Visual Data Mining Tool for gvSIG GIS (RVR, CPR, IYGH, AFM, JCTC), pp. 428–431.
SEKESEKE-2010-SilvaS #classification #data mining #knowledge base #modelling #relational #testing
Modeling and Testing a Knowledge Base for Instructing Users to Choose the Classification Task in Relational Data Mining (LMdS, AEAdS), pp. 608–613.
SIGIRSIGIR-2010-FengWZM #image #scalability #video
Mining adjacent markets from a large-scale ads video collection for image advertising (GF, XJW, LZ, WYM), pp. 893–894.
SIGIRSIGIR-2010-JiangPL #challenge #information retrieval #web
Search and browse log mining for web information retrieval: challenges, methods, and applications (DJ, JP, HL), p. 912.
SIGIRSIGIR-2010-LeeJSL #identification
Mining the blogosphere for top news stories identification (YL, HYJ, WS, JHL), pp. 395–402.
SPLCSPLC-2010-YoshimuraAF #constraints #feature model #identification
A Method to Identify Feature Constraints Based on Feature Selections Mining (KY, YA, TF), pp. 425–429.
ASEASE-2010-GibiecCC #query #towards
Towards mining replacement queries for hard-to-retrieve traces (MG, AC, JCH), pp. 245–254.
ASEASE-2010-HuangPWXM #assurance
Text mining in supporting software systems risk assurance (LH, DP, LW, TX, TM), pp. 163–166.
ASEASE-2010-LoM #specification
Scenario-based and value-based specification mining: better together (DL, SM), pp. 387–396.
ASEASE-2010-ShangAH #case study #experience #pipes and filters #repository #scalability #tool support #using
An experience report on scaling tools for mining software repositories using MapReduce (WS, BA, AEH), pp. 275–284.
ASEASE-2010-SindhgattaNSVR #developer
Timesheet assistant: mining and reporting developer effort (RS, NCN, BS, KV, AGR), pp. 265–274.
ICSEICSE-2010-DoanLMK #named #specification
LM: a miner for scenario-based specifications (TAD, DL, SM, SCK), pp. 319–320.
ICSEICSE-2010-HassanX #re-engineering
Mining software engineering data (AEH, TX), pp. 503–504.
ICSEICSE-2010-ZhongTXZW #api #migration
Mining API mapping for language migration (HZ, ST, TX, LZ, QW), pp. 195–204.
SACSAC-2010-BaralisCC #dataset #persistent #scalability
A persistent HY-Tree to efficiently support itemset mining on large datasets (EB, TC, SC), pp. 1060–1064.
SACSAC-2010-BlundoC #algorithm
A simple role mining algorithm (CB, SC), pp. 1958–1962.
SACSAC-2010-CeciALM #approach #data mining #ranking #relational
Complex objects ranking: a relational data mining approach (MC, AA, CL, DM), pp. 1071–1077.
SACSAC-2010-ChengSMZ #data mining #database #integration #named #online
PROM-OOGLE: data mining and integration of on-line databases to discover gene promoters (DC, JS, MMP, ORZ), pp. 1547–1551.
SACSAC-2010-CostaFGMO #learning #modelling
Mining models of exceptional objects through rule learning (GC, FF, MG, GM, RO), pp. 1078–1082.
SACSAC-2010-FlouvatSGRG
Constrained colocation mining: application to soil erosion characterization (FF, NSF, DG, IR, CG), pp. 1054–1059.
SACSAC-2010-GoethalsPM #database #relational #set
Mining interesting sets and rules in relational databases (BG, WLP, MM), pp. 997–1001.
SACSAC-2010-Kaneiwa #approach #information management #set
A rough set approach to mining connections from information systems (KK), pp. 990–996.
SACSAC-2010-KatevaLRSTR #architecture #data mining
SE-155 DBSA: a device-based software architecture for data mining (JK, PL, TR, JS, LT, JR), pp. 2273–2280.
SACSAC-2010-LeungHB #constraints #nondeterminism
Mining uncertain data for frequent itemsets that satisfy aggregate constraints (CKSL, BH, DAB), pp. 1034–1038.
SACSAC-2010-LiuJWZQ
Mining preorder relation between knowledge units from text (JL, LJ, ZW, QZ, YnQ), pp. 1047–1053.
SACSAC-2010-LuccheseOP #dataset #generative
A generative pattern model for mining binary datasets (CL, SO, RP), pp. 1109–1110.
SACSAC-2010-MarinhoCDFBBL #data mining #education #framework #ontology
An ontology-based software framework to provide educational data mining (TM, EdBC, DD, RF, LMB, IIB, HPLL), pp. 1433–1437.
SACSAC-2010-MengleG #category theory
Mining temporal relationships among categories (SSRM, NG), pp. 1107–1108.
SACSAC-2010-NingYPM #named #visualisation
OncoViz: a user-centric mining and visualization tool for cancer-related literature (ZN, JSY, MJP, AMM), pp. 1827–1828.
SACSAC-2010-RomaniAZCTT #algorithm #named
CLEARMiner: a new algorithm for mining association patterns on heterogeneous time series from climate data (LASR, AMHdÁ, JZJ, RC, CTJ, AJMT), pp. 900–905.
SACSAC-2010-SamiYPHH #api #detection
Malware detection based on mining API calls (AS, BY, HR, NP, SH, AH), pp. 1020–1025.
SACSAC-2010-ShieTY #data type #online
Online mining of temporal maximal utility itemsets from data streams (BES, VST, PSY), pp. 1622–1626.
CCCC-2010-JockschMSGA #compilation
Mining Opportunities for Code Improvement in a Just-In-Time Compiler (AJ, MM, JS, NG, JNA), pp. 10–25.
DACDAC-2010-LiFS #scalability #specification #verification
Scalable specification mining for verification and diagnosis (WL, AF, SAS), pp. 755–760.
DATEDATE-2010-VasudevanSPTTJ #automation #data mining #generative #named #static analysis #using
GoldMine: Automatic assertion generation using data mining and static analysis (SV, DS, SJP, DT, WT, DRJ), pp. 626–629.
HPDCHPDC-2010-HoblitzellMYFXB #biology
Text mining for bone biology (AH, SM, QY, SF, YX, JB), pp. 522–530.
PDPPDP-2010-KomashinskiyK #data mining #detection
Malware Detection by Data Mining Techniques Based on Positionally Dependent Features (DK, IVK), pp. 617–623.
ISSTAISSTA-2010-DallmeierKMHZ #generative #specification #testing
Generating test cases for specification mining (VD, NK, CM, SH, AZ), pp. 85–96.
TAPTAP-2010-ThummalapentaHTW #automation #generative #named #testing
DyGen: Automatic Generation of High-Coverage Tests via Mining Gigabytes of Dynamic Traces (ST, JdH, NT, SW), pp. 77–93.
TPDLECDL-2009-ChenRB #how #online #people #visualisation #web
How People Read Books Online: Mining and Visualizing Web Logs for Use Information (RC, AR, BBB), pp. 364–369.
ICDARICDAR-2009-KizuYTGS #2d #data mining
2D CAD Data Mining Based on Spatial Relation (HK, JY, TT, KG, NS), pp. 326–330.
SIGMODSIGMOD-2009-WangYGYTWLP #case study #communication #data mining #mobile #named
MobileMiner: a real world case study of data mining in mobile communication (TW, BY, JG, DY, ST, HW, KL, JP), pp. 1083–1086.
VLDBVLDB-2009-ChaudhuriGX #approximate #documentation
Mining Document Collections to Facilitate Accurate Approximate Entity Matching (SC, VG, DX), pp. 395–406.
VLDBVLDB-2009-ChenBN #evolution #maintenance #named #performance #web
NEAR-Miner: Mining Evolution Associations of Web Site Directories for Efficient Maintenance of Web Archives (LC, SSB, WN), pp. 1150–1161.
VLDBVLDB-2009-ChenLFCYH #graph #random #summary
Mining Graph Patterns Efficiently via Randomized Summaries (CC, CXL, MF, MC, XY, JH), pp. 742–753.
VLDBVLDB-2009-LeglerLSK #distributed #robust #using
Robust Distributed Top-N Frequent Pattern Mining Using the SAP BW Accelerator (TL, WL, JS, JK), pp. 1438–1449.
VLDBVLDB-2009-TatikondaP #manycore
Mining Tree-Structured Data on Multicore Systems (ST, SP), pp. 694–705.
VLDBVLDB-2009-WongCHKM #outsourcing
An Audit Environment for Outsourcing of Frequent Itemset Mining (WKW, DWLC, EH, BK, NM), pp. 1162–1172.
VLDBVLDB-2009-YangRW #execution #multi #streaming
A Shared Execution Strategy for Multiple Pattern Mining Requests over Streaming Data (DY, EAR, MOW), pp. 874–885.
EDMEDM-2009-AnayaB #approach #collaboration #data mining #framework
A Data Mining Approach to Reveal Representative Collaboration Indicators in Open Collaboration Frameworks (ARA, JB), pp. 210–219.
EDMEDM-2009-PechenizkiyTVAB #assessment #online #process
Process Mining Online Assessment Data (MP, NT, EV, WMPvdA, PDB), pp. 279–288.
EDMEDM-2009-RomeroVGCG #collaboration #data mining #education
Collaborative Data Mining Tool for Education (CR, SV, EG, CdC, MG), pp. 299–308.
EDMEDM-2009-SacinASO #data mining #education #recommendation #using
Recommendation in Higher Education Using Data Mining Techniques (CVS, JBA, LS, AO), pp. 191–199.
ICSMEICSM-2009-HuangSWSW #industrial #legacy #preprocessor
Preprocessing the noise in legacy user permission assignment data for role mining — An industrial practice (CH, JS, XW, YS, DW), pp. 403–406.
ICSMEICSM-2009-Zimmermann #debugging #development #predict #process
Changes and bugs — Mining and predicting development activities (TZ), pp. 443–446.
MSRMSR-2009-AnbalaganV #on the #repository
On mining data across software repositories (PA, MAV), pp. 171–174.
MSRMSR-2009-BajracharyaL #code search #topic
Mining search topics from a code search engine usage log (SKB, CVL), pp. 111–120.
MSRMSR-2009-BirdRBHGD #git
The promises and perils of mining git (CB, PCR, ETB, DJH, DMG, PTD), pp. 1–10.
MSRMSR-2009-EnslenHPV #analysis #automation #identifier #source code
Mining source code to automatically split identifiers for software analysis (EE, EH, LLP, KVS), pp. 71–80.
MSRMSR-2009-HattoriL
Mining the history of synchronous changes to refine code ownership (LH, ML), pp. 141–150.
MSRMSR-2009-HerzigZ #challenge #repository
Mining the Jazz repository: Challenges and opportunities (KH, AZ), pp. 159–162.
MSRMSR-2009-LinsteadB #debugging #gnome #modelling #statistics #topic
Mining the coherence of GNOME bug reports with statistical topic models (EL, PB), pp. 99–102.
MSRMSR-2009-ShangJAH #framework #pipes and filters #repository #research
MapReduce as a general framework to support research in Mining Software Repositories (MSR) (WS, ZMJ, BA, AEH), pp. 21–30.
WCREWCRE-1999-LoRRV99a #algorithm #evaluation #quantifier
Mining Quantified Temporal Rules: Formalism, Algorithms, and Evaluation (DL, GR, VPR, KV), pp. 62–71.
CoGCIG-2009-WeberM #approach #data mining #predict
A data mining approach to strategy prediction (BGW, MM), pp. 140–147.
HCIDHM-2009-NamA #data mining #image
Data Mining of Image Segments Data with Reduced Neurofuzzy System (DHN, EA), pp. 710–716.
HCIDHM-2009-PanCL #adaptation #behaviour #online #user interface
User Behavior Mining for On-Line GUI Adaptation (WP, YC, JL), pp. 275–284.
HCIHCI-NT-2009-TogawaKY #communication #detection #peer-to-peer #visualisation
Peer-to-Peer File Sharing Communication Detection System with Traffic Mining and Visualization (ST, KK, YY), pp. 900–909.
HCIHIMI-DIE-2009-HorvathLK #analysis #approach #data mining #usability
Usability Analyses of CRM Systems in Call Centers: The Data Mining Approach (ÁH, LL, AK), pp. 40–48.
HCIHIMI-II-2009-SagaTT #analysis #named
Loopo: Integrated Text Miner for FACT-Graph-Based Trend Analysis (RS, HT, KT), pp. 192–200.
ICEISICEIS-AIDSS-2009-Antunes
Mining Patterns in the Presence of Domain Knowledge (CA), pp. 188–193.
ICEISICEIS-AIDSS-2009-BiscarriMLGBM #detection #framework
A Mining Framework to Detect Non-technical Losses in Power Utilities (FB, IM, CL, JIG, JB, RM), pp. 96–101.
ICEISICEIS-AIDSS-2009-BratuP #data mining #preprocessor #towards
Towards a Unified Strategy for the Preprocessing Step in Data Mining (CVB, RP), pp. 230–235.
ICEISICEIS-AIDSS-2009-ChenC #data mining
The Role of Data Mining Techniques in Emergency Management (NC, AC), pp. 118–123.
ICEISICEIS-AIDSS-2009-Jou #algorithm #hybrid
A Projection-based Hybrid Sequential Patterns Mining Algorithm (CJ), pp. 152–157.
ICEISICEIS-AIDSS-2009-MarinicaOG #algorithm #using
User-driven Association Rule Mining using a Local Algorithm (CM, AO, FG), pp. 200–205.
ICEISICEIS-ISAS-2009-Petrusel #modelling #process #workflow
Mining and Modeling Decision Workflows from DSS User Activity Logs (RP), pp. 144–149.
ICEISICEIS-ISAS-2009-TatsiopoulosB #ontology
Ontology Mapping based on Association Rule Mining (CT, BB), pp. 33–40.
ICEISICEIS-J-2009-CunhaAM #data mining #reuse
Knowledge Reuse in Data Mining Projects and Its Practical Applications (RCLVC, PJLA, SRdLM), pp. 317–324.
ICEISICEIS-J-2009-FritzscheML #analysis #concept #data mining #design #industrial #interactive #quality
Interactive Quality Analysis in the Automotive Industry: Concept and Design of an Interactive, Web-Based Data Mining Application (SF, MM, CL), pp. 402–414.
ICEISICEIS-J-2009-MianiYSB #algorithm #fuzzy #ontology
NARFO Algorithm: Mining Non-redundant and Generalized Association Rules Based on Fuzzy Ontologies (RGM, CAY, MTPS, MB), pp. 415–426.
CIKMCIKM-2009-ChenPBSM #data mining #exclamation #lessons learnt
Practical lessons of data mining at Yahoo! (YC, DP, PB, AS, AM), pp. 1047–1056.
CIKMCIKM-2009-ChokG #framework #network #realtime
Spatio-temporal association rule mining framework for real-time sensor network applications (HC, LG), pp. 1761–1764.
CIKMCIKM-2009-GuoR #interactive #query
Mining linguistic cues for query expansion: applications to drug interaction search (SG, NR), pp. 335–344.
CIKMCIKM-2009-GwaderaC #ranking #using
Mining and ranking streams of news stories using cross-stream sequential patterns (RG, FC), pp. 1709–1712.
CIKMCIKM-2009-KasneciEW #named
MING: mining informative entity relationship subgraphs (GK, SE, GW), pp. 1653–1656.
CIKMCIKM-2009-MabroukehE #ontology #predict #semantics #using #web
Using domain ontology for semantic web usage mining and next page prediction (NRM, CIE), pp. 1677–1680.
CIKMCIKM-2009-PitonBBG #data mining
Domain driven data mining to improve promotional campaign ROI and select marketing channels (TP, JB, HB, FG), pp. 1057–1066.
CIKMCIKM-2009-PopescuGM
Mining tourist information from user-supplied collections (AP, GG, PAM), pp. 1713–1716.
CIKMCIKM-2009-SahlgrenK #social #social media
Terminology mining in social media (MS, JK), pp. 405–414.
CIKMCIKM-2009-Shi #adaptation #information retrieval #web
Adaptive web mining of bilingual lexicons for cross language information retrieval (LS), pp. 1561–1564.
CIKMCIKM-2009-SunMG #graph #independence #information retrieval
Independent informative subgraph mining for graph information retrieval (BS, PM, CLG), pp. 563–572.
CIKMCIKM-2009-TaoO #data type
Mining data streams with periodically changing distributions (YT, MTÖ), pp. 887–896.
CIKMCIKM-2009-TaoO09a #data type
Mining frequent itemsets in time-varying data streams (YT, MTÖ), pp. 1521–1524.
CIKMCIKM-2009-ZhangYWGGBYS #documentation #named #social #web
sDoc: exploring social wisdom for document enhancement in web mining (XZ, LY, XW, HG, ZG, SB, YY, ZS), pp. 395–404.
CIKMCIKM-2009-ZouLGZ #graph #nondeterminism
Frequent subgraph pattern mining on uncertain graph data (ZZ, JL, HG, SZ), pp. 583–592.
ECIRECIR-2009-OrlandoS #query
Mining Query Logs (SO, FS), pp. 814–817.
ECIRECIR-2009-UdupaSBB #information retrieval #query #quote
“They Are Out There, If You Know Where to Look”: Mining Transliterations of OOV Query Terms for Cross-Language Information Retrieval (RU, KS, AB, AB), pp. 437–448.
KDDKDD-2009-AggarwalLWW #nondeterminism
Frequent pattern mining with uncertain data (CCA, YL, JW, JW), pp. 29–38.
KDDKDD-2009-AhmedXCM #biology #modelling #topic
Structured correspondence topic models for mining captioned figures in biological literature (AA, EPX, WWC, RFM), pp. 39–48.
KDDKDD-2009-BerlingerioPNG #data analysis #interactive #workflow
Temporal mining for interactive workflow data analysis (MB, FP, MN, FG), pp. 109–118.
KDDKDD-2009-BerneckerKRVZ #database #nondeterminism #probability
Probabilistic frequent itemset mining in uncertain databases (TB, HPK, MR, FV, AZ), pp. 119–128.
KDDKDD-2009-ChenLTW #adaptation #concept
Extracting discriminative concepts for domain adaptation in text mining (BC, WL, IWT, TLW), pp. 179–188.
KDDKDD-2009-DaruruMWG #clustering #data flow #data mining #parallel #pervasive #scalability
Pervasive parallelism in data mining: dataflow solution to co-clustering large and sparse Netflix data (SD, NMM, MW, JG), pp. 1115–1124.
KDDKDD-2009-DeodharG #predict
Mining for the most certain predictions from dyadic data (MD, JG), pp. 249–258.
KDDKDD-2009-DingLZ
Entity discovery and assignment for opinion mining applications (XD, BL, LZ), pp. 1125–1134.
KDDKDD-2009-DuJDLT #approach #migration
Migration motif: a spatial — temporal pattern mining approach for financial markets (XD, RJ, LD, VEL, JHTJ), pp. 1135–1144.
KDDKDD-2009-Hand #data mining #modelling #tool support
Mismatched models, wrong results, and dreadful decisions: on choosing appropriate data mining tools (DJH), pp. 1–2.
KDDKDD-2009-HanhijarviOVPTM #data mining
Tell me something I don’t know: randomization strategies for iterative data mining (SH, MO, NV, KP, NT, HM), pp. 379–388.
KDDKDD-2009-JinHS #machine learning #named #novel #web
OpinionMiner: a novel machine learning system for web opinion mining and extraction (WJ, HHH, RKS), pp. 1195–1204.
KDDKDD-2009-Kumar #challenge #web
Mining web logs: applications and challenges (RK), pp. 3–4.
KDDKDD-2009-Last #data mining
Improving data mining utility with projective sampling (ML), pp. 487–496.
KDDKDD-2009-LiMPF #named #sequence #summary
DynaMMo: mining and summarization of coevolving sequences with missing values (LL, JM, NSP, CF), pp. 507–516.
KDDKDD-2009-LoCHKS #approach #behaviour #classification #detection
Classification of software behaviors for failure detection: a discriminative pattern mining approach (DL, HC, JH, SCK, CS), pp. 557–566.
KDDKDD-2009-Mannila #data mining
Randomization methods in data mining (HM), pp. 5–6.
KDDKDD-2009-MaunzHK #graph #refinement #scalability #using
Large-scale graph mining using backbone refinement classes (AM, CH, SK), pp. 617–626.
KDDKDD-2009-MonrealePTG #named #predict
WhereNext: a location predictor on trajectory pattern mining (AM, FP, RT, FG), pp. 637–646.
KDDKDD-2009-NijssenGR #approach #constraints #correlation #programming
Correlated itemset mining in ROC space: a constraint programming approach (SN, TG, LDR), pp. 647–656.
KDDKDD-2009-PatnaikMSR #data mining #using
Sustainable operation and management of data center chillers using temporal data mining (DP, MM, RKS, NR), pp. 1305–1314.
KDDKDD-2009-PoernomoG09a #fault tolerance #performance #towards
Towards efficient mining of proportional fault-tolerant frequent itemsets (AKP, VG), pp. 697–706.
KDDKDD-2009-ShenJY #matrix
Mining discrete patterns via binary matrix factorization (BHS, SJ, JY), pp. 757–766.
KDDKDD-2009-Srivastava #data mining
Data mining at NASA: from theory to applications (ANS), pp. 7–8.
KDDKDD-2009-SunPLCWLRY #estimation
Mining brain region connectivity for alzheimer’s disease study via sparse inverse covariance estimation (LS, RP, JL, KC, TW, JL, ER, JY), pp. 1335–1344.
KDDKDD-2009-WangCP #aspect-oriented #query
Mining broad latent query aspects from search sessions (XW, DC, KP), pp. 867–876.
KDDKDD-2009-XuYL #using
Named entity mining from click-through data using weakly supervised latent dirichlet allocation (GX, SHY, HL), pp. 1365–1374.
KDDKDD-2009-YeK #data mining
Time series shapelets: a new primitive for data mining (LY, EJK), pp. 947–956.
KDDKDD-2009-YooYLM #email #network #personalisation #social
Mining social networks for personalized email prioritization (SY, YY, FL, ICM), pp. 967–976.
KDDKDD-2009-ZhuM #web
Mining rich session context to improve web search (GZ, GM), pp. 1037–1046.
KDDKDD-2009-ZhuWKL
Augmenting the generalized hough transform to enable the mining of petroglyphs (QZ, XW, EJK, SHL), pp. 1057–1066.
KDIRKDIR-2009-EnguixDJ #natural language
DNA and Natural Languages — Text Mining (GBE, VD, MDJL), pp. 140–145.
KDIRKDIR-2009-FatemiPRS #concept #retrieval #semantics #using #video
Using Association Rule Mining to Enrich Semantic Concepts for Video Retrieval (NF, FP, LER, AFS), pp. 119–126.
KDIRKDIR-2009-Fernandez-FernandezG #query #taxonomy #topic
Hierarchical Taxonomy Extraction by Mining Topical Query Sessions (MFF, DGA), pp. 229–235.
KDIRKDIR-2009-PenaVP #data mining
Explorative Data Mining for the Sizing of Population Groups (IP, HLV, EP), pp. 152–159.
KDIRKDIR-2009-SaneifarBLPR
Mining for Relevant Terms from Log Files (HS, SB, AL, PP, MR), pp. 77–84.
KDIRKDIR-2009-WohrerZDB #composition #data mining #metaprogramming #optimisation #towards
Unboxing Data Mining Via Decomposition in Operators — Towards Macro Optimization and Distribution (AW, YZ, EuHD, PB), pp. 243–248.
KDIRKDIR-2009-Zhan #collaboration #data mining #privacy
Privacy-Preserving Collaborative Data Mining (JZ), p. 15.
KEODKEOD-2009-Zhan #collaboration #data mining #privacy
Privacy-Preserving Collaborative Data Mining (JZ), p. 15.
KMISKMIS-2009-Zhan #collaboration #data mining #privacy
Privacy-Preserving Collaborative Data Mining (JZ), p. 15.
MLDMMLDM-2009-CeciALCFVM #data type #detection #relational
Relational Frequent Patterns Mining for Novelty Detection from Data Streams (MC, AA, CL, CC, FF, CV, DM), pp. 427–439.
MLDMMLDM-2009-FernandezBFM #automation #data mining
Assisting Data Mining through Automated Planning (FF, DB, SF, DMM), pp. 760–774.
MLDMMLDM-2009-KovalevPV #image
Mining Lung Shape from X-Ray Images (VK, AP, PV), pp. 554–568.
MLDMMLDM-2009-LoglisciM #multi
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies (CL, DM), pp. 251–265.
MLDMMLDM-2009-MendesA #approach #natural language
Pattern Mining with Natural Language Processing: An Exploratory Approach (ACM, CA), pp. 266–279.
RecSysRecSys-2009-Tolomei #process #recommendation #web
Search the web x.0: mining and recommending web-mediated processes (GT), pp. 417–420.
SEKESEKE-2009-AlvaresOHB #data mining #framework #preprocessor
A Framework for Trajectory Data Preprocessing for Data Mining (LOA, GO, CAH, VB), pp. 698–702.
SEKESEKE-2009-AndresMN #realtime #testing #using
Using a Mining Frequency Patterns Model to Automate Passive Testing of Real-time Systems (CA, MGM, MN), pp. 426–431.
SEKESEKE-2009-CellierDFR #data mining #fault #locality #named #process
DeLLIS: A Data Mining Process for Fault Localization (PC, MD, SF, OR), pp. 432–437.
SEKESEKE-2009-VanHilstH #metric #process #repository
Mining Objective Process Metrics from Repository Data (MVH, SH), pp. 514–519.
SIGIRSIGIR-2009-WongLC #adaptation #detection #information management
Mining employment market via text block detection and adaptive cross-domain information extraction (TLW, WL, BC), pp. 283–290.
SIGIRSIGIR-2009-YeHL #approach #graph #multi #wiki #word
A graph-based approach to mining multilingual word associations from wikipedia (ZY, XH, HL), pp. 690–691.
SIGIRSIGIR-2009-ZhangWLOJH #dependence #parsing
Mining product reviews based on shallow dependency parsing (QZ, YW, TL, MO, JJ, XH), pp. 726–727.
ECMFAECMDA-FA-2009-KublerG #approach #qvt #using
A Pattern Mining Approach Using QVT (JK, TG), pp. 50–65.
MODELSMoDELS-2009-Booch #architecture
Architectural Mining: The Other Side of the MDD (GB), p. 627.
MODELSMoDELS-2009-Booch #architecture
Architectural Mining: The Other Side of the MDD (GB), p. 627.
ECOOPECOOP-2009-ZhongXZPM #api #named #recommendation
MAPO: Mining and Recommending API Usage Patterns (HZ, TX, LZ, JP, HM), pp. 318–343.
ASEASE-2009-AcharyaK #health #modelling #monitoring #performance
Mining Health Models for Performance Monitoring of Services (MA, VK), pp. 409–420.
ASEASE-2009-LoM #specification
Mining Hierarchical Scenario-Based Specifications (DL, SM), pp. 359–370.
ASEASE-2009-ThummalapentaX #detection #named
Alattin: Mining Alternative Patterns for Detecting Neglected Conditions (ST, TX), pp. 283–294.
ASEASE-2009-WasylkowskiZ #specification
Mining Temporal Specifications from Object Usage (AW, AZ), pp. 295–306.
ESEC-FSEESEC-FSE-2009-NguyenNPAN #graph #multi
Graph-based mining of multiple object usage patterns (TTN, HAN, NHP, JMAK, TNN), pp. 383–392.
ESEC-FSEESEC-FSE-2009-ThummalapentaXTHS #generative #named #object-oriented #source code #testing
MSeqGen: object-oriented unit-test generation via mining source code (ST, TX, NT, JdH, WS), pp. 193–202.
ICSEICSE-2009-ThummalapentaX #sequence
Mining exception-handling rules as sequence association rules (ST, TX), pp. 496–506.
SACSAC-2009-CaldersGPR #process #using
Using minimum description length for process mining (TC, CWG, MP, AR), pp. 1451–1455.
SACSAC-2009-EsfahaniMSU #process #using
Using process mining to business process distribution (FSE, MAAM, MNS, NIU), pp. 2140–2145.
SACSAC-2009-LinL #generative
Optimal candidate generation in spatial co-location mining (ZL, SL), pp. 1441–1445.
SACSAC-2009-LiuMYGF #corpus #probability
A sentence level probabilistic model for evolutionary theme pattern mining from news corpora (SL, YM, WGY, NG, OF), pp. 1742–1747.
SACSAC-2009-OwensMR #dataset
Capturing truthiness: mining truth tables in binary datasets (CCOI, TMM, NR), pp. 1467–1474.
SACSAC-2009-ShangLL #biology #functional #network
Mining functional associated patterns from biological network data (XS, ZL, WL), pp. 1488–1489.
PPoPPPPoPP-2009-MaA #compilation #data mining #runtime
A compiler and runtime system for enabling data mining applications on gpus (WM, GA), pp. 287–288.
SOSPSOSP-2009-XuHFPJ #detection #problem #scalability
Detecting large-scale system problems by mining console logs (WX, LH, AF, DAP, MIJ), pp. 117–132.
FASEFASE-2009-AcharyaX #api #source code #specification
Mining API Error-Handling Specifications from Source Code (MA, TX), pp. 370–384.
TACASTACAS-2009-GouesW #specification
Specification Mining with Few False Positives (CLG, WW), pp. 292–306.
ISSTAISSTA-2009-ChengLZWY #debugging #graph #identification #using
Identifying bug signatures using discriminative graph mining (HC, DL, YZ, XW, XY), pp. 141–152.
ISSTAISSTA-2009-JiangS #automation #random testing #testing
Automatic mining of functionally equivalent code fragments via random testing (LJ, ZS), pp. 81–92.
DocEngDocEng-2008-CodocedoA #documentation #information management #repository
No mining, no meaning: relating documents across repositories with ontology-driven information extraction (VC, HA), pp. 110–118.
DRRDRR-2008-Schomaker #word
Word mining in a sparsely labeled handwritten collection (LRBS), p. 68150.
HTHT-2008-KolakS #generative
Generating links by mining quotations (OK, BNS), pp. 117–126.
SIGMODSIGMOD-2008-PatelHL #classification
Mining relationships among interval-based events for classification (DP, WH, MLL), pp. 393–404.
SIGMODSIGMOD-2008-Ramaswamy #data mining
Extreme data mining (SR), pp. 1–2.
SIGMODSIGMOD-2008-SunWYCHYZ #named #network
BibNetMiner: mining bibliographic information networks (YS, TW, ZY, HC, JH, XY, PZ), pp. 1341–1344.
SIGMODSIGMOD-2008-WangPTT #named #visualisation
CSV: visualizing and mining cohesive subgraphs (NW, SP, KLT, AKHT), pp. 445–458.
SIGMODSIGMOD-2008-YanCHY #graph
Mining significant graph patterns by leap search (XY, HC, JH, PSY), pp. 433–444.
VLDBVLDB-2008-Bar-YossefG #query
Mining search engine query logs via suggestion sampling (ZBY, MG), pp. 54–65.
VLDBVLDB-2008-DingTSWK #comparison #distance #metric #query
Querying and mining of time series data: experimental comparison of representations and distance measures (HD, GT, PS, XW, EJK), pp. 1542–1552.
VLDBVLDB-2008-LoK #specification
Mining patterns and rules for software specification discovery (DL, SCK), pp. 1609–1616.
VLDBVLDB-2008-ZhangPWN #correlation #higher-order
Mining non-redundant high order correlations in binary data (XZ, FP, WW, ABN), pp. 1178–1188.
EDMEDM-2008-AbbasS #database #relational #towards
Towards Argument Mining from Relational DataBase (SA, HS), pp. 202–209.
EDMEDM-2008-AllevatoTEP #automation #testing
Mining Data from an Automated Grading and Testing System by Adding Rich Reporting Capabilities (AA, MT, SHE, MAPQ), pp. 167–176.
EDMEDM-2008-HubscherP #data mining
Integrating Knowledge Gained From Data Mining With Pedagogical Knowledge (RH, SP), pp. 97–106.
EDMEDM-2008-JeongB #behaviour #modelling #student
Mining Student Behavior Models in Learning-by-Teaching Environments (HJ, GB), pp. 127–136.
EDMEDM-2008-PechenizkiyCVB #assessment #case study #student
Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case Study (MP, TC, EV, PDB), pp. 187–191.
EDMEDM-2008-RomeroGFV #education #visualisation
Mining and Visualizing Visited Trails in Web-Based Educational Systems (CR, SGS, MF, SV), pp. 182–186.
EDMEDM-2008-RomeroVEH #algorithm #data mining #student
Data Mining Algorithms to Classify Students (CR, SV, PGE, CH), pp. 8–17.
EDMEDM-2008-ZhangMDTVC
Mining Free-form Spoken Responses to Tutor Prompts (XZ, JM, ND, CT, JV, ATC), pp. 234–241.
SIGITESIGITE-2008-Kalathur #data mining #education #experience #online #student
Enriching student experience with student driven content while teaching an online data mining class (SK), pp. 125–130.
CSMRCSMR-2008-FulopFG #benchmark #design pattern #metric #tool support #towards
Towards a Benchmark for Evaluating Design Pattern Miner Tools (LJF, RF, TG), pp. 143–152.
ICPCICPC-2008-BhattiDR #object-oriented
Aspect Mining in Procedural Object Oriented Code (MUB, SD, AR), pp. 230–235.
ICSMEICSM-2008-Lo #execution #specification
Mining specifications in diversified formats from execution traces (DL), pp. 420–423.
MSRMSR-2008-Bernstein #data mining #how
How to learn enough data mining to be dangerous in 60 minutes (AB), pp. 77–78.
MSRMSR-2008-HillFBSNPV #automation #maintenance #named #source code #tool support
AMAP: automatically mining abbreviation expansions in programs to enhance software maintenance tools (EH, ZPF, HB, GS, YN, LLP, KVS), pp. 79–88.
MSRMSR-2008-LaymanNGBB #analysis #visual notation
Mining software effort data: preliminary analysis of visual studio team system data (LL, NN, SG, JB, AB), pp. 43–46.
MSRMSR-2008-SchulerZ
Mining usage expertise from version archives (DS, TZ), pp. 121–124.
MSRMSR-2008-ThummalapentaX #detection #framework #named #open source #repository #web
SpotWeb: detecting framework hotspots via mining open source repositories on the web (ST, TX), pp. 109–112.
PASTEPASTE-2008-LoM #modelling #specification
Specification mining of symbolic scenario-based models (DL, SM), pp. 29–35.
WCREWCRE-2008-IshioDMI #detection #java #source code
Mining Coding Patterns to Detect Crosscutting Concerns in Java Programs (TI, HD, TM, KI), pp. 123–132.
WCREWCRE-2008-MensKK
Pitfalls in Aspect Mining (KM, AK, JK), pp. 113–122.
ICEISICEIS-AIDSS-2008-BiscarriMLGBM #data mining #variability
A Data Mining Method Based on the Variability of the Customer Consumption — A Special Application on Electric Utility Companies (FB, IM, CL, JIG, JB, RM), pp. 370–374.
ICEISICEIS-AIDSS-2008-BrissonC #data mining #ontology #process
An Ontology Driven Data Mining Process (LB, MC), pp. 54–61.
ICEISICEIS-AIDSS-2008-IngvaldsenG #process
EVS Process Miner: Incorporating Ideas from Search, ETL into Process Mining (JEI, JAG), pp. 340–347.
ICEISICEIS-AIDSS-2008-JansLV #case study #data mining #reduction
Internal Fraud Risk Reduction — Results of a Data Mining Case Study (MJ, NL, KV), pp. 161–166.
ICEISICEIS-AIDSS-2008-NguyenG #approach #evolution #multi
Rule Evolution Approach for Mining Multivariate Time Series Data (VAN, VG), pp. 19–26.
ICEISICEIS-DISI-2008-Gorea #architecture #concept #data mining #modelling #named #web
DeVisa — Concepts and Architecture of a Data Mining Models Scoring and Management Web System (DG), pp. 276–281.
ICEISICEIS-ISAS2-2008-Nikovski #concurrent #process #representation #workflow
Workflow Trees for Representation and Mining of Implicitly Concurrent Business Processes (DN), pp. 30–36.
ICEISICEIS-ISAS2-2008-SchultK #adaptation #automation #concept #data mining #enterprise #self
Self-Adaptive Customizing with Data Mining Methods — A Concept for the Automatic Customizing of an ERP System with Data Mining Methods (RS, GK), pp. 70–75.
ICEISICEIS-J-2008-BrissonC08a #data mining #how #process #question #semantics
How to Semantically Enhance a Data Mining Process? (LB, MC), pp. 103–116.
CIKMCIKM-2008-Agrawal #data mining
Humane data mining (RA), pp. 1–2.
CIKMCIKM-2008-AlqadahB #algorithm #clustering #effectiveness
An effective algorithm for mining 3-clusters in vertically partitioned data (FA, RB), pp. 1103–1112.
CIKMCIKM-2008-LinL #clique #performance
Fast spatial co-location mining without cliqueness checking (ZL, SL), pp. 1461–1462.
CIKMCIKM-2008-LiWT #documentation #effectiveness #taxonomy
Effective pattern taxonomy mining in text documents (YL, STW, XT), pp. 1509–1510.
CIKMCIKM-2008-LiZBXL #information management
A two-stage text mining model for information filtering (YL, XZ, PB, YX, RYKL), pp. 1023–1032.
CIKMCIKM-2008-LoekitoB #behaviour
Mining influential attributes that capture class and group contrast behaviour (EL, JB), pp. 971–980.
CIKMCIKM-2008-MaYLK #network #process #social #using
Mining social networks using heat diffusion processes for marketing candidates selection (HM, HY, MRL, IK), pp. 233–242.
CIKMCIKM-2008-MiaoLD #integration
An integration strategy for mining product features and opinions (QM, QL, RD), pp. 1369–1370.
CIKMCIKM-2008-TanbeerAJL #data type #performance
Efficient frequent pattern mining over data streams (SKT, CFA, BSJ, YKL), pp. 1447–1448.
CIKMCIKM-2008-TongSEF #performance
Fast mining of complex time-stamped events (HT, YS, TER, CF), pp. 759–768.
CIKMCIKM-2008-UdupaSKJ #corpus
Mining named entity transliteration equivalents from comparable corpora (RU, KS, AK, JJ), pp. 1423–1424.
CIKMCIKM-2008-WangZ #effectiveness #query
Mining term association patterns from search logs for effective query reformulation (XW, CZ), pp. 479–488.
ECIRECIR-2008-Berendt #documentation #information retrieval #web
You Are a Document Too: Web Mining and IR for Next-Generation Information Literacy (BB), p. 3.
ECIRECIR-2008-SchedlKPW #automation #information management #music #towards #web
Towards an Automatically Generated Music Information System Via Web Content Mining (MS, PK, TP, GW), pp. 585–590.
ECIRECIR-2008-SongHRB #composition #modelling #multi #query #using
Facilitating Query Decomposition in Query Language Modeling by Association Rule Mining Using Multiple Sliding Windows (DS, QH, SMR, PB), pp. 334–345.
KDDKDD-2008-BifetG #adaptation #data type
Mining adaptively frequent closed unlabeled rooted trees in data streams (AB, RG), pp. 34–42.
KDDKDD-2008-BlockeelCFGPR #database #induction #prototype
An inductive database prototype based on virtual mining views (HB, TC, ÉF, BG, AP, CR), pp. 1061–1064.
KDDKDD-2008-CaoJPHLCL #query
Context-aware query suggestion by mining click-through and session data (HC, DJ, JP, QH, ZL, EC, HL), pp. 875–883.
KDDKDD-2008-ChoRC #data mining #identification #network
Reconstructing chemical reaction networks: data mining meets system identification (YJC, NR, YC), pp. 142–150.
KDDKDD-2008-FanZCGYHYV #modelling
Direct mining of discriminative and essential frequent patterns via model-based search tree (WF, KZ, HC, JG, XY, JH, PSY, OV), pp. 230–238.
KDDKDD-2008-GrossmanG #data mining #performance #using
Data mining using high performance data clouds: experimental studies using sector and sphere (RLG, YG), pp. 920–927.
KDDKDD-2008-GuptaFFSK #algorithm #approximate #evaluation
Quantitative evaluation of approximate frequent pattern mining algorithms (RG, GF, BF, MS, VK), pp. 301–309.
KDDKDD-2008-HwangKRZ #graph
Bridging centrality: graph mining from element level to group level (WH, TK, MR, AZ), pp. 336–344.
KDDKDD-2008-JiangPLCH
Mining preferences from superior and inferior examples (BJ, JP, XL, DWC, JH), pp. 390–398.
KDDKDD-2008-KotsifakosNVT #data mining #modelling #named
Pattern-Miner: integrated management and mining over data mining models (EEK, IN, YV, YT), pp. 1081–1084.
KDDKDD-2008-LingMZS #multi #topic
Mining multi-faceted overviews of arbitrary topics in a text collection (XL, QM, CZ, BRS), pp. 497–505.
KDDKDD-2008-PedreschiRT #data mining
Discrimination-aware data mining (DP, SR, FT), pp. 560–568.
KDDKDD-2008-RaedtGN #constraints #programming
Constraint programming for itemset mining (LDR, TG, SN), pp. 204–212.
KDDKDD-2008-SaigoKT #graph
Partial least squares regression for graph mining (HS, NK, KT), pp. 578–586.
KDDKDD-2008-ShaoCTYA #performance #sequence
Efficient ticket routing by resolution sequence mining (QS, YC, ST, XY, NA), pp. 605–613.
KDDKDD-2008-ShengPI #data mining #multi #quality #using
Get another label? improving data quality and data mining using multiple, noisy labelers (VSS, FJP, PGI), pp. 614–622.
KDDKDD-2008-ShiehK #named
iSAX: indexing and mining terabyte sized time series (JS, EJK), pp. 623–631.
KDDKDD-2008-TangZYLZS #named #network #social
ArnetMiner: extraction and mining of academic social networks (JT, JZ, LY, JL, LZ, ZS), pp. 990–998.
KDDKDD-2008-TongPSYF #graph #named #performance #scalability
Colibri: fast mining of large static and dynamic graphs (HT, SP, JS, PSY, CF), pp. 686–694.
KDDKDD-2008-ZhangZS #categorisation #concept #data type
Categorizing and mining concept drifting data streams (PZ, XZ, YS), pp. 812–820.
RecSysRecSys-2008-ShaniCM #recommendation #web
Mining recommendations from the web (GS, DMC, CM), pp. 35–42.
SEKESEKE-2008-BahsoonE
An Example on Economics-driven Software Mining (RB, WE), pp. 375–380.
SEKESEKE-2008-DingH #algorithm #named #performance
VP: an Efficient Algorithm for Frequent Itemset Mining (QD, WSH), pp. 381–386.
SEKESEKE-2008-WuSF #clustering #re-engineering
Discovering Meaningful Clusters from Mining the Software Engineering Literature (YW, HPS, LF), pp. 613–618.
SIGIRSIGIR-2008-HuCBLCY #query #web
Web query translation via web log mining (RH, WC, PB, YL, ZC, QY), pp. 749–750.
SIGIRSIGIR-2008-ZhouQLBQC #named #overview
SOPING: a Chinese customer review mining system (CZ, GQ, KL, JB, MQ, CC), pp. 741–742.
RERE-2008-Castro-HerreraDCM #data mining #elicitation #process #recommendation #requirements #scalability #using
Using Data Mining and Recommender Systems to Facilitate Large-Scale, Open, and Inclusive Requirements Elicitation Processes (CCH, CD, JCH, BM), pp. 165–168.
ASEASE-2008-LoM
Mining Scenario-Based Triggers and Effects (DL, SM), pp. 109–118.
ASEASE-2008-ThummalapentaX #detection #framework #named #open source #source code #web
SpotWeb: Detecting Framework Hotspots and Coldspots via Mining Open Source Code on the Web (ST, TX), pp. 327–336.
FSEFSE-2008-GabelS #automation #named
Javert: fully automatic mining of general temporal properties from dynamic traces (MG, ZS), pp. 339–349.
ICSEICSE-2008-GabelS #specification
Symbolic mining of temporal specifications (MG, ZS), pp. 51–60.
ICSEICSE-2008-SankaranarayananIG #induction #library #logic programming #specification #using
Mining library specifications using inductive logic programming (SS, FI, AG), pp. 131–140.
ICSEICSE-2008-SchaferJM #framework
Mining framework usage changes from instantiation code (TS, JJ, MM), pp. 471–480.
SACSAC-2008-AntonyBHP #approach #biology #interactive #using #validation
Computational approach to biological validation of protein-protein interactions discovered using literature mining (AA, SB, SH, MJP), pp. 1302–1306.
SACSAC-2008-LaRosaXM #kernel
Frequent pattern mining for kernel trace data (CL, LX, KM), pp. 880–885.
SACSAC-2008-LiZC #case study
Mining disease-specific molecular association profiles from biomedical literature: a case study (JL, XZ, JYC), pp. 1287–1291.
SACSAC-2008-MontanaTT #algorithm #data type
Data stream mining for market-neutral algorithmic trading (GM, KT, TT), pp. 966–970.
SACSAC-2008-WeiSWS #email #forensics #identification
Mining spam email to identify common origins for forensic application (CW, AS, GW, AS), pp. 1433–1437.
SACSAC-2008-ZengLL #fault tolerance
Mining fault-tolerant frequent patterns efficiently with powerful pruning (JJZ, GL, CCL), pp. 927–931.
CASECASE-2008-Balasundaram #graph
Cohesive subgroup model for graph-based text mining (BB), pp. 989–994.
CASECASE-2008-BenedettoDSW #automation #verification
Automatic verification of wireless control in a mining ventilation system (MDDB, AD, ES, EW), pp. 858–863.
CASECASE-2008-FischionePRST #architecture #automation #communication
Mining ventilation automation: Wireless sensing, communication architecture and advanced services (CF, LP, CR, FS, ST), pp. 851–857.
CASECASE-2008-WitrantJ #modelling
Air flow modeling in deepwells: Application to mining ventilation (EW, KHJ), pp. 845–850.
DATEDATE-2008-ChengH #invariant #verification
Simulation-Directed Invariant Mining for Software Verification (XC, MSH), pp. 682–687.
HPDCHPDC-2008-ShrinivasN #data mining #detection #grid
Issues in applying data mining to grid job failure detection and diagnosis (LS, JFN), pp. 239–240.
HPDCHPDC-2008-XiaFJTW #approach #correlation #evaluation #file system #named #novel #optimisation #performance
FARMER: a novel approach to file access correlation mining and evaluation reference model for optimizing peta-scale file system performance (PX, DF, HJ, LT, FW), pp. 185–196.
PDPPDP-2008-GrossF #communication #data mining #feedback #named #ubiquitous
CoDaMine: Communication Data Mining for Feedback and Control in Ubiquitous Environments (TG, MF), pp. 539–546.
PPoPPPPoPP-2008-TatikondaP #adaptation #approach #architecture #manycore #memory management
An adaptive memory conscious approach for mining frequent trees: implications for multi-core architectures (ST, SP), pp. 263–264.
ICSTICST-2008-ZaidmanRDD #co-evolution #repository
Mining Software Repositories to Study Co-Evolution of Production & Test Code (AZ, BVR, SD, AvD), pp. 220–229.
ICDARICDAR-2007-CeciBPM #approach #data mining #detection #order
A Data Mining Approach to Reading Order Detection (MC, MB, GP, DM), pp. 924–928.
JCDLJCDL-2007-MimnoM #library
Mining a digital library for influential authors (DMM, AM), pp. 105–106.
JCDLJCDL-2007-SandersonW #library #process
Integrating data and text mining processes for digital library applications (RS, PBW), pp. 73–79.
JCDLJCDL-2007-StewartCB #corpus #generative #scalability
A new generation of textual corpora: mining corpora from very large collections (GS, GRC, AB), pp. 356–365.
JCDLJCDL-2007-ZhuangELG #quality
Measuring conference quality by mining program committee characteristics (ZZ, EE, DL, CLG), pp. 225–234.
SIGMODSIGMOD-2007-FaloutsosKS #graph #matrix #scalability #tool support #using
Mining large graphs and streams using matrix and tensor tools (CF, TGK, JS), p. 1174.
SIGMODSIGMOD-2007-VardeRS #data mining #named #optimisation #process #visual notation
AutoDomainMine: a graphical data mining system for process optimization (ASV, EAR, RDSJ), pp. 1103–1105.
VLDBVLDB-2007-GonzalezHLMS #adaptation #approach #network #performance
Adaptive Fastest Path Computation on a Road Network: A Traffic Mining Approach (HG, JH, XL, MM, JPS), pp. 794–805.
VLDBVLDB-2007-LiH #approximate #multi
Mining Approximate Top-K Subspace Anomalies in Multi-Dimensional Time-Series Data (XL, JH), pp. 447–458.
VLDBVLDB-2007-LiL #multi #optimisation
Optimization of Frequent Itemset Mining on Multiple-Core Processor (EL, LL), pp. 1275–1285.
VLDBVLDB-2007-WongCHKM #outsourcing #security
Security in Outsourcing of Association Rule Mining (WKW, DWC, EH, BK, NM), pp. 111–122.
CSMRCSMR-2007-GrossoPG #approach #database
An approach for mining services in database oriented applications (CDG, MDP, IGRdG), pp. 287–296.
ICPCICPC-2007-KagdiMS #repository #traceability
Mining Software Repositories for Traceability Links (HHK, JIM, BS), pp. 145–154.
ICPCICPC-2007-RoyURD #case study
Evaluating Aspect Mining Techniques: A Case Study (CKR, MGU, BR, TRD), pp. 167–176.
ICSMEICSM-2007-AntoniolGMT #evolution
Mining the Lexicon Used by Programmers during Sofware Evolution (GA, YGG, EM, PT), pp. 14–23.
MSRMSR-2007-KagdiCM #source code
Comparing Approaches to Mining Source Code for Call-Usage Patterns (HHK, MLC, JIM), p. 20.
MSRMSR-2007-KieferBT #evolution #ontology #repository
Mining Software Repositories with iSPAROL and a Software Evolution Ontology (CK, AB, JT), p. 10.
MSRMSR-2007-LinsteadRBLB #developer #eclipse #modelling #topic
Mining Eclipse Developer Contributions via Author-Topic Models (EL, PR, SKB, CVL, PB), p. 30.
MSRMSR-2007-Masticola #development #lightweight #repository #using
Lightweight Risk Mitigation for Software Development Projects Using Repository Mining (SPM), p. 13.
MSRMSR-2007-MorisakiMMTM #data analysis #fault
Defect Data Analysis Based on Extended Association Rule Mining (SM, AM, TM, HT, KiM), p. 3.
MSRMSR-2007-Robbes #repository
Mining a Change-Based Software Repository (RR), p. 15.
MSRMSR-2007-WeissgerberPB #data mining #detection #developer #how #visual notation
Visual Data Mining in Software Archives to Detect How Developers Work Together (PW, MP, MB), p. 9.
MSRMSR-2007-YuR #cvs #developer #open source #repository
Mining CVS Repositories to Understand Open-Source Project Developer Roles (LY, SR), p. 8.
MSRMSR-2007-Zimmermann #cvs
Mining Workspace Updates in CVS (TZ), p. 11.
WCREWCRE-2007-RaberL #data mining #debugging #using
Emulated Breakpoint Debugger and Data Mining Using Detours (JR, EL), pp. 271–272.
DiGRADiGRA-2007-BraggeS #game studies #profiling #research #tool support #using
Profiling Academic Research on Digital Games Using Text Mining Tools (JB, JS).
HCIHCI-AS-2007-XueL #data mining #distributed #modelling #research #similarity
Research of Distributed Data Mining Association Rules Model Based on Similarity (SX, ZL), pp. 1180–1189.
HCIHCI-MIE-2007-ChevrinC #interactive #multi
Dynamic Association Rules Mining to Improve Intermediation Between User Multi-channel Interactions and Interactive e-Services (VC, OC), pp. 265–274.
HCIHIMI-IIE-2007-KanekoNATT #analysis #generative #using
A Method for Generating Plans for Retail Store Improvements Using Text Mining and Conjoint Analysis (TK, YN, MA, TT, YT), pp. 910–917.
HCIHIMI-MTT-2007-HuangLW #correlation #multi
Mining Attack Correlation Scenarios Based on Multi-agent System (SH, ZL, LW), pp. 632–641.
HCIHIMI-MTT-2007-NishimuraH #approach #case study #data mining #fault #visualisation
The Study of Past Working History Visualization for Supporting Trial and Error Approach in Data Mining (KN, MH), pp. 327–334.
HCIHIMI-MTT-2007-TogawaKY #communication #detection #network #peer-to-peer #using
Peer-to-Peer File Sharing Communication Detection System Using Network Traffic Mining (ST, KK, YY), pp. 769–778.
ICEISICEIS-AIDSS-2007-Aalst #analysis #process #roadmap #verification
Trends in business process analysis — from verification to process mining (WMPvdA), pp. 5–9.
ICEISICEIS-AIDSS-2007-BecksH #analysis #industrial
Trend Analysis Based on Explorative Data and Text Mining: A Decision Support System for the European Home Textile Industry (AB, JH), pp. 253–258.
ICEISICEIS-AIDSS-2007-SokolovaF #architecture #assessment #data mining #multi
A Multi-Agent Architecture for Environmental Impact Assessment: Information Fusion, Data Mining and Decision Making (MVS, AFC), pp. 219–224.
ICEISICEIS-AIDSS-2007-WanzellerB #assessment #data mining #similarity
Similarity Assessment in a CBR Application for Clickstream Data Mining Plans Selection (CW, OB), pp. 137–144.
ICEISICEIS-DISI-2007-Aalst #analysis #process #roadmap #verification
Trends in business process analysis — from verification to process mining (WMPvdA), pp. 5–9.
ICEISICEIS-EIS-2007-Aalst #analysis #process #roadmap #verification
Trends in business process analysis — from verification to process mining (WMPvdA), pp. 5–9.
ICEISICEIS-HCI-2007-Aalst #analysis #process #roadmap #verification
Trends in business process analysis — from verification to process mining (WMPvdA), pp. 5–9.
ICEISICEIS-SAIC-2007-Aalst #analysis #process #roadmap #verification
Trends in business process analysis — from verification to process mining (WMPvdA), pp. 5–9.
CIKMCIKM-2007-AngiulliF #performance
Very efficient mining of distance-based outliers (FA, FF), pp. 791–800.
CIKMCIKM-2007-DonZGTACSP #visualisation
Discovering interesting usage patterns in text collections: integrating text mining with visualization (AD, EZ, MG, ST, LA, TC, BS, CP), pp. 213–222.
CIKMCIKM-2007-ParkR #information retrieval #multi #web
Mining web multi-resolution community-based popularity for information retrieval (LAFP, KR), pp. 545–554.
CIKMCIKM-2007-WuHK #web
Mining redundancy in candidate-bearing snippets to improve web question answering (YW, XH, HK), pp. 999–1002.
KDDKDD-2007-ArchakGI #exclamation #power of
Show me the money!: deriving the pricing power of product features by mining consumer reviews (NA, AG, PGI), pp. 56–65.
KDDKDD-2007-CotofreiS #data mining #probability #process
Stochastic processes and temporal data mining (PC, KS), pp. 183–190.
KDDKDD-2007-DouFRFMT #development #framework #ontology
Development of NeuroElectroMagnetic ontologies(NEMO): a framework for mining brainwave ontologies (DD, GAF, JR, RMF, ADM, DMT), pp. 270–279.
KDDKDD-2007-Fayyad #internet #web
From mining the web to inventing the new sciences underlying the internet (UMF), pp. 2–3.
KDDKDD-2007-GaoESCX #consistency #data mining #problem #set
The minimum consistent subset cover problem and its applications in data mining (BJG, ME, JyC, OS, HX), pp. 310–319.
KDDKDD-2007-GiannottiNPP
Trajectory pattern mining (FG, MN, FP, DP), pp. 330–339.
KDDKDD-2007-GuoZXF #data mining #database #learning #multimodal
Enhanced max margin learning on multimodal data mining in a multimedia database (ZG, ZZ, EPX, CF), pp. 340–349.
KDDKDD-2007-JanssensGM #analysis #clustering #hybrid
Dynamic hybrid clustering of bioinformatics by incorporating text mining and citation analysis (FALJ, WG, BDM), pp. 360–369.
KDDKDD-2007-Kleinberg #challenge #network #privacy #process #social
Challenges in mining social network data: processes, privacy, and paradoxes (JMK), pp. 4–5.
KDDKDD-2007-LiLW #equivalence #statistics
Mining statistically important equivalence classes and delta-discriminative emerging patterns (JL, GL, LW), pp. 430–439.
KDDKDD-2007-LoKL #performance #specification
Efficient mining of iterative patterns for software specification discovery (DL, SCK, CL), pp. 460–469.
KDDKDD-2007-NijssenF
Mining optimal decision trees from itemset lattices (SN, ÉF), pp. 530–539.
KDDKDD-2007-PanZZPSPY #modelling #network
Domain-constrained semi-supervised mining of tracking models in sensor networks (RP, JZ, VWZ, JJP, DS, SJP, QY), pp. 1023–1027.
KDDKDD-2007-Parthasarathy #data mining #learning
Data mining at the crossroads: successes, failures and learning from them (SP), pp. 1053–1055.
KDDKDD-2007-SunFPY #graph #named #scalability
GraphScope: parameter-free mining of large time-evolving graphs (JS, CF, SP, PSY), pp. 687–696.
KDDKDD-2007-TongFK #graph #performance #proximity
Fast direction-aware proximity for graph mining (HT, CF, YK), pp. 747–756.
KDDKDD-2007-WangZHS #coordination #correlation #topic
Mining correlated bursty topic patterns from coordinated text streams (XW, CZ, XH, RS), pp. 784–793.
KDDKDD-2007-WongFPW
Mining favorable facets (RCWW, JP, AWCF, KW), pp. 804–813.
KDDKDD-2007-ZhaoDZ #network
Mining complex power networks for blackout prevention (JHZ, ZYD, PZ), pp. 986–994.
KDDKDD-2007-ZhaoMY
Mining templates from search result records of search engines (HZ, WM, CTY), pp. 884–893.
MLDMMLDM-2007-AoYHH #data type
Mining Maximal Frequent Itemsets in Data Streams Based on FP-Tree (FA, YY, JH, KH), pp. 479–489.
MLDMMLDM-2007-GellerPK
Blog Mining for the Fortune 500 (JG, SP, SK), pp. 379–391.
MLDMMLDM-2007-Morzy #predict
Mining Frequent Trajectories of Moving Objects for Location Prediction (MM), pp. 667–680.
MLDMMLDM-2007-PalancarTCL #algorithm #distributed #memory management #parallel
Distributed and Shared Memory Algorithm for Parallel Mining of Association Rules (JHP, OFT, JFC, RHL), pp. 349–363.
MLDMMLDM-2007-SadoddinG #case study #comparative #data mining #detection #machine learning
A Comparative Study of Unsupervised Machine Learning and Data Mining Techniques for Intrusion Detection (RS, AAG), pp. 404–418.
MLDMMLDM-2007-TanN #data mining #privacy #probability #re-engineering
Generic Probability Density Function Reconstruction for Randomization in Privacy-Preserving Data Mining (VYFT, SKN), pp. 76–90.
MLDMMLDM-2007-WangLYC #classification #data type #fuzzy #incremental
An Incremental Fuzzy Decision Tree Classification Method for Mining Data Streams (TW, ZL, YY, HC), pp. 91–103.
MLDMMLDM-2007-WangXC #approach #classification #novel
A Novel Rule Ordering Approach in Classification Association Rule Mining (YJW, QX, FC), pp. 339–348.
RecSysRecSys-2007-SandvigMB #collaboration #recommendation #robust
Robustness of collaborative recommendation based on association rule mining (JJS, BM, RDB), pp. 105–112.
SIGIRSIGIR-2007-DingL
The utility of linguistic rules in opinion mining (XD, BL), pp. 811–812.
PLDIPLDI-2007-RamanathanGJ #specification #using
Static specification inference using predicate mining (MKR, AG, SJ), pp. 123–134.
ASEASE-2007-Kagdi #fine-grained #predict #source code
Improving change prediction with fine-grained source code mining (HHK), pp. 559–562.
ASEASE-2007-KagdiCM #approach
An approach to mining call-usage patternswith syntactic context (HHK, MLC, JIM), pp. 457–460.
ASEASE-2007-LinsteadRBLB #concept #modelling #probability #topic
Mining concepts from code with probabilistic topic models (EL, PR, SKB, CVL, PB), pp. 461–464.
ASEASE-2007-LoMK #execution #specification
Mining modal scenario-based specifications from execution traces of reactive systems (DL, SM, SCK), pp. 465–468.
ESEC-FSEESEC-FSE-2007-AcharyaXPX #api #partial order #source code #specification
Mining API patterns as partial orders from source code: from usage scenarios to specifications (MA, TX, JP, JX), pp. 25–34.
ESEC-FSEESEC-FSE-2007-ChristodorescuJK #behaviour #specification
Mining specifications of malicious behavior (MC, SJ, CK), pp. 5–14.
ICSEICSE-2007-GanapathyKJJ #concept analysis #legacy #using
Mining Security-Sensitive Operations in Legacy Code Using Concept Analysis (VG, DK, TJ, SJ), pp. 458–467.
SACSAC-2007-CaldersGM
Mining itemsets in the presence of missing values (TC, BG, MM), pp. 404–408.
SACSAC-2007-FattaF #data mining #distributed #multi
A customizable multi-agent system for distributed data mining (GDF, GF), pp. 42–47.
SACSAC-2007-HwangY #ranking
Mining and processing category ranking (SwH, HY), pp. 441–442.
SACSAC-2007-KimHK #approach #folksonomy
FCA-based approach for mining contextualized folksonomy (HLK, ShH, HGK), pp. 1340–1345.
SACSAC-2007-Knijf #named
FAT-miner: mining frequent attribute trees (JDK), pp. 417–422.
SACSAC-2007-LinL #privacy #transaction
Privacy preserving itemset mining through fake transactions (JLL, JYCL), pp. 375–379.
SACSAC-2007-TengC #community #mobile
Mining communities of acquainted mobile users on call detail records (WGT, MCC), pp. 957–958.
SACSAC-2007-XiongCL #classification #database #multi #using
Mining multiple private databases using a kNN classifier (LX, SC, LL), pp. 435–440.
CASECASE-2007-RamachandranEMB #data mining #health
Data Mining in Military Health Systems — Clinical and Administrative Applications (SR, ME, RJM, PCB), pp. 158–163.
DACDAC-2007-WangBA #correlation #data mining #perspective
Design-Silicon Timing Correlation A Data Mining Perspective (LCW, PB, MSA), pp. 384–389.
DATEDATE-2007-BabighianKV #data mining #interactive #optimisation
Interactive presentation: PowerQuest: trace driven data mining for power optimization (PB, GK, MYV), pp. 1078–1083.
PPoPPPPoPP-2007-BuehrerPTKS #architecture #towards
Toward terabyte pattern mining: an architecture-conscious solution (GB, SP, ST, TMK, JHS), pp. 2–12.
ISSTAISSTA-2007-ShohamYFP #abstraction #specification #using
Static specification mining using automata-based abstractions (SS, EY, SF, MP), pp. 174–184.
DocEngDocEng-2006-ZeridaLC
Combining linguistic and structural descriptors for mining biomedical literature (NZ, NL, BC), pp. 62–64.
TPDLECDL-2006-RodriguezLGM #library
The SINAMED and ISIS Projects: Applying Text Mining Techniques to Improve Access to a Medical Digital Library (MdBR, MJML, DG, JMV), pp. 548–551.
HTHT-2006-KazienkoP #assessment #web
Hyperlink assessment based on web usage mining (PK, MP), pp. 85–88.
JCDLJCDL-2006-DownieH #library #music #overview
Review mining for music digital libraries: phase II (JSD, XH), pp. 196–197.
JCDLJCDL-2006-PlaisantRYAKSCL #interface #visual notation
Exploring erotics in Emily Dickinson’s correspondence with text mining and visual interfaces (CP, JR, BY, LA, MGK, MNS, TC, GL), pp. 141–150.
SIGMODSIGMOD-2006-BorgwardtBK #graph #named #visual notation
VGM: visual graph mining (KMB, SB, HPK), pp. 733–735.
VLDBVLDB-2006-JiTT #3d #dataset
Mining Frequent Closed Cubes in 3D Datasets (LJ, KLT, AKHT), pp. 811–822.
VLDBVLDB-2006-Keogh #database #scalability
A Decade of Progress in Indexing and Mining Large Time Series Databases (EJK), p. 1268.
VLDBVLDB-2006-LeglerLR #data mining
Data Mining with the SAP Netweaver BI Accelerator (TL, WL, AR), pp. 1059–1068.
VLDBVLDB-2006-RodriguesTTFL #graph #interactive #named #scalability #visualisation
GMine: A System for Scalable, Interactive Graph Visualization and Mining (JFRJ, HT, AJMT, CF, JL), pp. 1195–1198.
VLDBVLDB-2006-SteinleAGL #dependence #generative #novel
Mapping Moving Landscapes by Mining Mountains of Logs: Novel Techniques for Dependency Model Generation (MS, KA, SG, CL), pp. 1093–1102.
ITiCSEITiCSE-2006-MarkovR #data mining
An introduction to the WEKA data mining system (ZM, IR), pp. 367–368.
ICPCICPC-2006-SafyallahS #dynamic analysis #execution #using
Dynamic Analysis of Software Systems using Execution Pattern Mining (HS, KS), pp. 84–88.
ICPCICPC-2006-ZaidmanBD #comprehension #how #metric
How Webmining and Coupling Metrics Improve Early Program Comprehension (AZ, BDB, SD), pp. 74–78.
ICSMEICSM-2006-Hassan #developer #repository
Mining Software Repositories to Assist Developers and Support Managers (AEH), pp. 339–342.
MSRMSR-2006-BirdGDGS #email #network #social
Mining email social networks (CB, AG, PTD, MG, AS), pp. 137–143.
MSRMSR-2006-BirdGDGS06a #email #network #social
Mining email social networks in Postgres (CB, AG, PTD, MG, AS), pp. 185–186.
MSRMSR-2006-BreuZL #eclipse
Mining eclipse for cross-cutting concerns (SB, TZ, CL), pp. 94–97.
MSRMSR-2006-HammoudaK #repository
Concern based mining of heterogeneous software repositories (IH, KK), pp. 80–86.
MSRMSR-2006-KagdiYM #sequence #version control
Mining sequences of changed-files from version histories (HHK, SY, JIM), pp. 47–53.
MSRMSR-2006-KimZKHMGPWZ #named #repository
TA-RE: an exchange language for mining software repositories (SK, TZ, MK, AEH, AM, TG, MP, EJWJ, AZ), pp. 22–25.
MSRMSR-2006-RoblesGMA #evolution #scalability
Mining large software compilations over time: another perspective of software evolution (GR, JMGB, MM, JJA), pp. 3–9.
MSRMSR-2006-VoineaT06a #repository
Mining software repositories with CVSgrab (LV, ACT), pp. 167–168.
MSRMSR-2006-WeissgerberDG #refactoring
Mining refactorings in ARGOUML (PW, SD, CG), pp. 175–176.
MSRMSR-2006-XieP #api #named #open source #repository
MAPO: mining API usages from open source repositories (TX, JP), pp. 54–57.
MSRMSR-2006-YangR #architecture #evolution
Scenarios for mining the software architecture evolution (YY, CR), pp. 10–13.
MSRMSR-2006-ZimmermannBLL
Mining additions of method calls in ArgoUML (TZ, SB, CL, VBL), pp. 169–170.
MSRMSR-2006-ZimmermannKZW
Mining version archives for co-changed lines (TZ, SK, AZ, EJWJ), pp. 72–75.
SCAMSCAM-2006-FulopGF #c++ #design pattern #tool support
Evaluating C++ Design Pattern Miner Tools (LJF, TG, RF), pp. 127–138.
WCREWCRE-2006-Krinke #control flow #graph
Mining Control Flow Graphs for Crosscutting Concerns (JK), pp. 334–342.
WCREWCRE-2006-MarinMD #framework
A common framework for aspect mining based on crosscutting concern sorts (MM, LM, AvD), pp. 29–38.
WCREWCRE-2006-MarinMD06a #named #tool support
FINT: Tool Support for Aspect Mining (MM, LM, AvD), pp. 299–300.
ICEISICEIS-AIDSS-2006-BoudjeloudP #data mining #interactive
Semi Interactive Method for Data Mining (LB, FP), pp. 3–10.
ICEISICEIS-AIDSS-2006-GollerHS #algorithm #data mining
Beneficial Sequential Combination of Data Mining Algorithms (MG, MH, MS), pp. 135–143.
ICEISICEIS-AIDSS-2006-PintoGS #data mining #database #paradigm
Data Mining as a New Paradigm for Business Intelligence in Database Marketing Projects (FMP, PG, MFS), pp. 144–149.
ICEISICEIS-HCI-2006-BadjioP #analysis #data mining #process #visual notation
Context of Use Analysis — Activity Checklist for Visual Data Mining (EPFB, FP), pp. 45–50.
ICEISICEIS-ISAS-2006-DaoHHRV #abstraction #modelling #tool support #towards #uml
Towards Practical Tools for Mining Abstractions in UML Models (MD, MH, MRH, CR, PV), pp. 276–283.
CIKMCIKM-2006-GonzalezHL #set #workflow
Mining compressed commodity workflows from massive RFID data sets (HG, JH, XL), pp. 162–171.
CIKMCIKM-2006-HuangTOC #on the
On progressive sequential pattern mining (JWH, CYT, JCO, MSC), pp. 850–851.
CIKMCIKM-2006-HuZZ #array #clustering #identification #integration
Integration of cluster ensemble and EM based text mining for microarray gene cluster identification and annotation (XH, XZ, XZ), pp. 824–825.
CIKMCIKM-2006-KapoorPTT #database #distributed #privacy
Privacy preserving sequential pattern mining in distributed databases (VK, PP, FT, MT), pp. 758–767.
CIKMCIKM-2006-KimC #concept #named #similarity #taxonomy
CP/CV: concept similarity mining without frequency information from domain describing taxonomies (JWK, KSC), pp. 483–492.
CIKMCIKM-2006-KunkleZC #performance
Efficient mining of max frequent patterns in a generalized environment (DK, DZ, GC), pp. 810–811.
CIKMCIKM-2006-QamraTC #clustering #using
Mining blog stories using community-based and temporal clustering (AQ, BLT, EYC), pp. 58–67.
CIKMCIKM-2006-TatikondaPK #algorithm
TRIPS and TIDES: new algorithms for tree mining (ST, SP, TMK), pp. 455–464.
CIKMCIKM-2006-ZhangW #array
Mining coherent patterns from heterogeneous microarray data (XZ, WW), pp. 838–839.
CIKMCIKM-2006-ZhuangJZ #overview #summary
Movie review mining and summarization (LZ, FJ, XZ), pp. 43–50.
ICMLICML-2006-TsudaK #clustering #graph
Clustering graphs by weighted substructure mining (KT, TK), pp. 953–960.
ICPRICPR-v1-2006-BethelHG
Mining for Implications in Medical Data (CLB, LOH, DBG), pp. 1212–1215.
ICPRICPR-v1-2006-CantoniLL #challenge #data mining #distributed #network
Challenges for Data Mining in Distributed Sensor Networks (VC, LL, PL), pp. 1000–1007.
ICPRICPR-v2-2006-YuW06b #nondeterminism
Mining Uncertain Data in Low-dimensional Subspace (ZY, HSW), pp. 748–751.
ICPRICPR-v3-2006-NockLS #incremental #statistics
Statistical Borders for Incremental Mining (RN, PAL, JES), pp. 212–215.
ICPRICPR-v3-2006-VilchesEVT #data mining #recognition
Data Mining Applied to Acoustic Bird Species Recognition (EV, IAE, EEV, CET), pp. 400–403.
ICPRICPR-v3-2006-ZhouK #detection #multi #video
Unusual Event Detection via Multi-camera Video Mining (HZ, DK), pp. 1161–1166.
KDDKDD-2006-AggarwalPZ #data mining #on the #privacy
On privacy preservation against adversarial data mining (CCA, JP, BZ), pp. 510–516.
KDDKDD-2006-AgichteinZ #behaviour #identification #web
Identifying “best bet” web search results by mining past user behavior (EA, ZZ), pp. 902–908.
KDDKDD-2006-BuehrerPG
Out-of-core frequent pattern mining on a commodity PC (GB, SP, AG), pp. 86–95.
KDDKDD-2006-CaldersGJ #set
Mining rank-correlated sets of numerical attributes (TC, BG, SJ), pp. 96–105.
KDDKDD-2006-CaruanaEMRSFHK #predict
Mining citizen science data to predict orevalence of wild bird species (RC, MFE, AM, MR, DS, DF, WMH, SK), pp. 909–915.
KDDKDD-2006-Cavaretta #challenge #data mining
Data mining challenges in the automotive domain (MC), p. 836.
KDDKDD-2006-FormanKS
Pragmatic text mining: minimizing human effort to quantify many issues in call logs (GF, EK, JS), pp. 852–861.
KDDKDD-2006-GionisMMT #data mining
Assessing data mining results via swap randomization (AG, HM, TM, PT), pp. 167–176.
KDDKDD-2006-GuoV #multi #relational #validation
Mining relational data through correlation-based multiple view validation (HG, HLV), pp. 567–573.
KDDKDD-2006-HashimotoAUKM #order #performance #probability
A new efficient probabilistic model for mining labeled ordered trees (KH, KFAK, NU, MK, HM), pp. 177–186.
KDDKDD-2006-HettichP #lessons learnt
Mining for proposal reviewers: lessons learned at the national science foundation (SH, MJP), pp. 862–871.
KDDKDD-2006-HorvathRW #graph
Frequent subgraph mining in outerplanar graphs (TH, JR, SW), pp. 197–206.
KDDKDD-2006-JiangG #data type #named
CFI-Stream: mining closed frequent itemsets in data streams (NJ, LG), pp. 592–597.
KDDKDD-2006-KeCN #approach #correlation #using
Mining quantitative correlated patterns using an information-theoretic approach (YK, JC, WN), pp. 227–236.
KDDKDD-2006-LingSBM #development
Maximum profit mining and its application in software development (CXL, VSS, TFWB, NHM), pp. 929–934.
KDDKDD-2006-LoekitoB #diagrams #performance #using
Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams (EL, JB), pp. 307–316.
KDDKDD-2006-ManiyarN #algorithm #data mining #using #visual notation #visualisation
Visual data mining using principled projection algorithms and information visualization techniques (DMM, ITN), pp. 643–648.
KDDKDD-2006-McCallum #data mining #information management
Information extraction, data mining and joint inference (AM), p. 835.
KDDKDD-2006-MeiZ
A mixture model for contextual text mining (QM, CZ), pp. 649–655.
KDDKDD-2006-MierswaWKSE #agile #data mining #named #prototype
YALE: rapid prototyping for complex data mining tasks (IM, MW, RK, MS, TE), pp. 935–940.
KDDKDD-2006-Morchen #algorithm
Algorithms for time series knowledge mining (FM), pp. 668–673.
KDDKDD-2006-PalatinLSW #grid
Mining for misconfigured machines in grid systems (NP, AL, AS, RW), pp. 687–692.
KDDKDD-2006-PanGBXTF #automation #image
Automatic mining of fruit fly embryo images (JYP, AGRB, EPX, AJMT, CF), pp. 693–698.
KDDKDD-2006-Piatetsky-ShapiroGDFGZ #challenge #data mining #question
Is there a grand challenge or X-prize for data mining? (GPS, RG, CD, RF, LG, MJZ), pp. 954–956.
KDDKDD-2006-TanSZ
Mining long-term search history to improve search accuracy (BT, XS, CZ), pp. 718–723.
KDDKDD-2006-TaoXZ #database #metric #scalability
Mining distance-based outliers from large databases in any metric space (YT, XX, SZ), pp. 394–403.
KDDKDD-2006-WangYPYY #concept #data type
Suppressing model overfitting in mining concept-drifting data streams (HW, JY, JP, PSY, JXY), pp. 736–741.
KDDKDD-2006-ZhangHL
Mining progressive confident rules (MZ, WH, MLL), pp. 803–808.
KDDKDD-2006-ZhaoLBX #data mining #identification
Opportunity map: identifying causes of failure — a deployed data mining system (KZ, BL, JB, WX), pp. 892–901.
KDDKDD-2006-ZhaoZR #framework #named
BLOSOM: a framework for mining arbitrary boolean expressions (LZ, MJZ, NR), pp. 827–832.
SEKESEKE-2006-BogornyEA #framework #named #preprocessor
GEOARM: an Interoperable Framework to Improve Geographic Data Preprocessing and Spatial Association Rule Mining (VB, PME, LOA), pp. 79–84.
SEKESEKE-2006-CazellaA #architecture #data mining #multi #recommendation #research
An architecture based on multi-agent system and data mining for recommending research papers and researchers (SCC, LOCA), pp. 67–72.
SEKESEKE-2006-GaaloulG #analysis #statistics #workflow
A Workflow Mining Tool based on Logs Statistical Analysis (WG, CG), pp. 595–600.
SEKESEKE-2006-HungL #data mining #performance #using
Using Data Mining Schemes for Improvement on System Performance in Virtual Environments (SSH, DSML), pp. 61–66.
SEKESEKE-2006-SartipiS #analysis #concept #evaluation #execution
Application of Execution Pattern Mining and Concept Lattice Analysis on Software Structure Evaluation (KS, HS), pp. 302–308.
SIGIRSIGIR-2006-SunOC #dependence #query #retrieval
Mining dependency relations for query expansion in passage retrieval (RS, CHO, TSC), pp. 382–389.
OOPSLAOOPSLA-2006-SahavechaphanC #named
XSnippet: mining For sample code (NS, KTC), pp. 413–430.
LOPSTRLOPSTR-2006-AbdennadherOST #automation #named
ARM: Automatic Rule Miner (SA, AO, NS, AT), pp. 17–25.
ASEASE-2006-BreuZ #aspect-oriented #version control
Mining Aspects from Version History (SB, TZ), pp. 221–230.
FSEFSE-2006-LoK #named #robust #scalability #specification #towards
SMArTIC: towards building an accurate, robust and scalable specification miner (DL, SCK), pp. 265–275.
ICSEICSE-2006-NagappanBZ #component #metric #predict
Mining metrics to predict component failures (NN, TB, AZ), pp. 452–461.
ICSEICSE-2006-YangEBBD #api #named
Perracotta: mining temporal API rules from imperfect traces (JY, DE, DB, TB, MD), pp. 282–291.
SACSAC-2006-AbidinP #classification #data mining #named #nearest neighbour #performance #scalability
SMART-TV: a fast and scalable nearest neighbor based classifier for data mining (TA, WP), pp. 536–540.
SACSAC-2006-FengQWZ #query #xml
Exploit sequencing to accelerate hot XML query pattern mining (JF, QQ, JW, LZ), pp. 517–524.
SACSAC-2006-GamaP #data mining #data type
Discretization from data streams: applications to histograms and data mining (JG, CP), pp. 662–667.
SACSAC-2006-GiannottiNPP #sequence
Mining sequences with temporal annotations (FG, MN, DP, FP), pp. 593–597.
SACSAC-2006-GuoW #data mining #on the #privacy #using
On the use of spectral filtering for privacy preserving data mining (SG, XW), pp. 622–626.
SACSAC-2006-MaC #algorithm #array #data mining #network #novel
A novel data mining algorithm for reconstructing gene regulatory networks from microarray data (PCHM, KCCC), pp. 202–203.
SACSAC-2006-TsengCL #e-commerce #navigation #personalisation #predict
Mining and prediction of temporal navigation patterns for personalized services in e-commerce (VST, JCC, KWL), pp. 867–871.
SACSAC-2006-WuH #interactive #network
Mining and analyzing the topological structure of protein-protein interaction networks (DDW, XH), pp. 185–189.
SACSAC-2006-ZhouHCPB
Approaches to text mining for clinical medical records (XZ, HH, IC, AP, ADB), pp. 235–239.
DACDAC-2006-WuH #bound #constraints #equivalence
Mining global constraints for improving bounded sequential equivalence checking (WW, MSH), pp. 743–748.
HPCAHPCA-2006-JaleelMJ #case study #data mining #parallel #performance
Last level cache (LLC) performance of data mining workloads on a CMP — a case study of parallel bioinformatics workloads (AJ, MM, BLJ), pp. 88–98.
HPDCHPDC-2006-CieslakTC #data mining #distributed
Troubleshooting Distributed Systems via Data Mining (DAC, DT, NVC), pp. 309–312.
PODSPODS-2005-CormodeM #multi #performance
Space efficient mining of multigraph streams (GC, SM), pp. 271–282.
PODSPODS-2005-LiLWFT #data mining #perspective
Relative risk and odds ratio: a data mining perspective (HL, JL, LW, MF, YPT), pp. 368–377.
SIGMODSIGMOD-2005-CongTTX
Mining Top-k Covering Rule Groups for Gene Expression Data (GC, KLT, AKHT, XX), pp. 670–681.
SIGMODSIGMOD-2005-GovindarajuRM #approximate #performance #using
Fast and Approximate Stream Mining of Quantiles and Frequencies Using Graphics Processors (NKG, NR, DM), pp. 611–622.
SIGMODSIGMOD-2005-LuoTWZ #data type #sql
A native extension of SQL for mining data streams (CL, HT, HW, CZ), pp. 873–875.
SIGMODSIGMOD-2005-SakuraiPF #correlation #named
BRAID: Stream Mining through Group Lag Correlations (YS, SP, CF), pp. 599–610.
SIGMODSIGMOD-2005-ZhangKCY #sequence
Mining Periodic Patterns with Gap Requirement from Sequences (MZ, BK, DWLC, KYY), pp. 623–633.
SIGMODSIGMOD-2005-ZhaoZ #3d #algorithm #array #clustering #effectiveness #named
TriCluster: An Effective Algorithm for Mining Coherent Clusters in 3D Microarray Data (LZ, MJZ), pp. 694–705.
VLDBVLDB-2005-ChiWY #data type #named
Loadstar: Load Shedding in Data Stream Mining (YC, HW, PSY), pp. 1303–1305.
VLDBVLDB-2005-CormodeMR #data type
Summarizing and Mining Inverse Distributions on Data Streams via Dynamic Inverse Sampling (GC, SM, IR), pp. 25–36.
VLDBVLDB-2005-GhotingBPKNCD
Cache-conscious Frequent Pattern Mining on a Modern Processor (AG, GB, SP, DK, ADN, YKC, PD), pp. 577–588.
VLDBVLDB-2005-XinHYC #set
Mining Compressed Frequent-Pattern Sets (DX, JH, XY, HC), pp. 709–720.
ITiCSEITiCSE-2005-Roiger #data mining #education
Teaching an introductory course in data mining (RJR), p. 415.
CSMRCSMR-2005-ZaidmanCDP #comprehension #execution #process
Applying Webmining Techniques to Execution Traces to Support the Program Comprehension Process (AZ, TC, SD, JP), pp. 134–142.
ICSMEICSM-2005-FerencBFL #design pattern #machine learning
Design Pattern Mining Enhanced by Machine Learning (RF, ÁB, LJF, JL), pp. 295–304.
IWPCIWPC-2005-CeccatoMMMTT #comparison
A Qualitative Comparison of Three Aspect Mining Techniques (MC, MM, KM, LM, PT, TT), pp. 13–22.
MSRMSR-2005-AntoniolRV #linear #predict #repository
Linear predictive coding and cepstrum coefficients for mining time variant information from software repositories (GA, VFR, GV), pp. 61–65.
MSRMSR-2005-FischerORG #evolution #product line
Mining evolution data of a product family (MF, JO, JR, HCG), pp. 36–40.
MSRMSR-2005-GermanCS #comprehension #development #framework #tool support
A framework for describing and understanding mining tools in software development (DMG, DC, MADS), pp. 31–35.
MSRMSR-2005-HayesDS #feedback #how #re-engineering
Text mining for software engineering: how analyst feedback impacts final results (JHH, AD, SKS), pp. 56–60.
MSRMSR-2005-HuangL #learning #process #verification #version control
Mining version histories to verify the learning process of Legitimate Peripheral Participants (SKH, KmL), pp. 21–25.
MSRMSR-2005-KagdiCM #repository #source code #taxonomy #towards
Towards a taxonomy of approaches for mining of source code repositories (HHK, MLC, JIM), pp. 26–30.
MSRMSR-2005-MierleLRW #cvs #performance #repository #student
Mining student CVS repositories for performance indicators (KM, KL, STR, GW), pp. 105–109.
MSRMSR-2005-OhbaG #concept #identifier #keyword #scalability #towards
Toward mining “concept keywords” from identifiers in large software projects (MO, KG), pp. 11–15.
MSRMSR-2005-SpaccoSHP #automation #programming #repository #testing
Software repository mining with Marmoset: an automated programming project snapshot and testing system (JS, JS, DH, WP), pp. 46–50.
MSRMSR-2005-VanHilstGL #process #repository
Repository mining and Six Sigma for process improvement (MVH, PKG, CL), pp. 101–104.
MSRMSR-2005-YingWA #eclipse #repository #source code
Source code that talks: an exploration of Eclipse task comments and their implication to repository mining (ATTY, JLW, SA), pp. 91–95.
SCAMSCAM-2005-Breu
Extending Dynamic Aspect Mining with Static Information (SB), pp. 57–65.
TLCATLCA-2005-MatwinFHC #data mining #formal method #privacy #using
Privacy in Data Mining Using Formal Methods (SM, APF, ITH, VC), pp. 278–292.
SOFTVISSOFTVIS-2005-BurchDW #data mining #visual notation
Visual data mining in software archives (MB, SD, PW), pp. 37–46.
ICEISICEIS-v2-2005-DoP #dataset #scalability #visualisation
Mining Very Large Datasets with SVM and Visualization (TND, FP), pp. 127–141.
ICEISICEIS-v2-2005-KianmehrZNOA #approach #data mining #network
Combining Neural Network and Support Vector Machine into Integrated Approach for Biodata Mining (KK, HZ, KN, , RA), pp. 182–187.
ICEISICEIS-v2-2005-KumarKDB #clustering #using #web
Web Usage Mining Using Rough Agglomerative Clustering (PK, PRK, SKD, RSB), pp. 315–320.
ICEISICEIS-v2-2005-SantosPS #clustering #data mining #framework #modelling
A Cluster Framework for Data Mining Models — An Application to Intensive Medicine (MFS, JP, ÁMS), pp. 163–168.
ICEISICEIS-v3-2005-EspositoMORS #data mining #detection #novel #realtime
Real Time Detection of Novel Attacks by Means of Data Mining Techniques (ME, CM, FO, SPR, CS), pp. 120–127.
ICEISICEIS-v5-2005-BadjioP #data mining #metric #quality #tool support #visual notation
Visual Data Mining Tools: Quality Metrics Definition and Application (EPFB, FP), pp. 98–103.
CIKMCIKM-2005-Ahonen-Myka #sequence #set #word
Mining all maximal frequent word sequences in a set of sentences (HAM), pp. 255–256.
CIKMCIKM-2005-CarinoJLWY #machine learning #web
Mining officially unrecognized side effects of drugs by combining web search and machine learning (CC, YJ, BL, PMW, CTY), pp. 365–372.
CIKMCIKM-2005-HuangLH #approach #distributed #permutation
A new permutation approach for distributed association rule mining (YH, ZL, HH), pp. 351–352.
CIKMCIKM-2005-HuYSZS #semantics
Mining undiscovered public knowledge from complementary and non-interactive biomedical literature through semantic pruning (XH, IY, MS, YZ, IYS), pp. 249–250.
CIKMCIKM-2005-KumamotoT #people #web
Web opinion poll: extracting people’s view by impression mining from the web (TK, KT), pp. 265–266.
CIKMCIKM-2005-LiL #community
Mining community structure of named entities from free text (XL, BL), pp. 275–276.
CIKMCIKM-2005-LiuZ #relational #xml
Relational computation for mining association rules from XML data (HCL, JZ), pp. 253–254.
CIKMCIKM-2005-WangHL #database #framework
A framework for mining topological patterns in spatio-temporal databases (JW, WH, MLL), pp. 429–436.
CIKMCIKM-2005-ZhaoBG #query #xml
Mining conserved XML query paths for dynamic-conscious caching (QZ, SSB, LG), pp. 219–220.
ECIRECIR-2005-Baeza-Yates #query #web
Applications of Web Query Mining (RABY), pp. 7–22.
KDDKDD-2005-BesemannD #integration #markov
Integration of profile hidden Markov model output into association rule mining (CB, AD), pp. 538–543.
KDDKDD-2005-ChenW #web
Web mining from competitors’ websites (XC, YfBW), pp. 550–555.
KDDKDD-2005-CongHP #parallel
Parallel mining of closed sequential patterns (SC, JH, DAP), pp. 562–567.
KDDKDD-2005-FanLH #image #learning #semantics #statistics
Mining images on semantics via statistical learning (JF, HL, MSH), pp. 22–31.
KDDKDD-2005-Fulgoni #internet
Mining the internet: the eighth wonder of the world (GF), p. 2.
KDDKDD-2005-GoethalsHB #graph #query
Mining tree queries in a graph (BG, EH, JVdB), pp. 61–69.
KDDKDD-2005-JinSA #information management #optimisation
Simultaneous optimization of complex mining tasks with a knowledgeable cache (RJ, KS, GA), pp. 600–605.
KDDKDD-2005-KalosR #data mining #industrial
Data mining in the chemical industry (ANK, TR), pp. 763–769.
KDDKDD-2005-KoppelSZ #fault
Determining an author’s native language by mining a text for errors (MK, JS, KZ), pp. 624–628.
KDDKDD-2005-LiFHCJMWSK
Mining risk patterns in medical data (JL, AWCF, HH, JC, HJ, DM, GJW, RS, CK), pp. 770–775.
KDDKDD-2005-LiLMP #framework
An integrated framework on mining logs files for computing system management (TL, FL, SM, WP), pp. 776–781.
KDDKDD-2005-MeiZ
Discovering evolutionary theme patterns from text: an exploration of temporal text mining (QM, CZ), pp. 198–207.
KDDKDD-2005-MorinagaAISA #dependence #semantics
Key semantics extraction by dependency tree mining (SM, HA, TI, YS, SA), pp. 666–671.
KDDKDD-2005-NasraouiCR #metric #retrieval #similarity #using #web
Using retrieval measures to assess similarity in mining dynamic web clickstreams (ON, CC, CR), pp. 439–448.
KDDKDD-2005-PeiJZ #clique #on the
On mining cross-graph quasi-cliques (JP, DJ, AZ), pp. 228–238.
KDDKDD-2005-Petrushin #multi #self #using #video
Mining rare and frequent events in multi-camera surveillance video using self-organizing maps (VAP), pp. 794–800.
KDDKDD-2005-Scholz
Sampling-based sequential subgroup mining (MS), pp. 265–274.
KDDKDD-2005-SilvaZS #probability #workflow
Probabilistic workflow mining (RBdAeS, JZ, JGS), pp. 275–284.
KDDKDD-2005-TaoZ #corpus #integration
Mining comparable bilingual text corpora for cross-language information integration (TT, CZ), pp. 691–696.
KDDKDD-2005-YamanishiM #monitoring #network
Dynamic syslog mining for network failure monitoring (KY, YM), pp. 499–508.
KDDKDD-2005-YangPM #framework
A generalized framework for mining spatio-temporal patterns in scientific data (HY, SP, SM), pp. 716–721.
KDDKDD-2005-YanZH #constraints #graph #relational
Mining closed relational graphs with connectivity constraints (XY, XJZ, JH), pp. 324–333.
KDDKDD-2005-ZakiPAS #algorithm #category theory #clustering #dataset #effectiveness #named
CLICKS: an effective algorithm for mining subspace clusters in categorical datasets (MJZ, MP, IA, TS), pp. 736–742.
KDDKDD-2005-ZakiR #reasoning #set #using
Reasoning about sets using redescription mining (MJZ, NR), pp. 364–373.
MLDMMLDM-2005-AdegoriteBKS #approach
An Approach to Mining Picture Objects Based on Textual Cues (AIA, OAB, MSK, KBS), pp. 466–475.
MLDMMLDM-2005-BichindaritzA #concept
Concept Mining for Indexing Medical Literature (IB, SA), pp. 682–691.
MLDMMLDM-2005-GillamA
Pattern Mining Across Domain-Specific Text Collections (LG, KA), pp. 570–579.
MLDMMLDM-2005-KarrasMGO
Improved MRI Mining by Integrating Support Vector Machine Priors in the Bayesian Restoration (DAK, BGM, DGD, DvO), pp. 325–333.
MLDMMLDM-2005-KuhlmannVLT #data mining #simulation
Data Mining on Crash Simulation Data (AK, RMV, CL, CAT), pp. 558–569.
MLDMMLDM-2005-MottlKSM #data mining #kernel #multi
Principles of Multi-kernel Data Mining (VM, OK, OS, IBM), pp. 52–61.
MLDMMLDM-2005-PrayR
Mining Expressive Temporal Associations from Complex Data (KAP, CR), pp. 384–394.
MLDMMLDM-2005-SharmaVTV #approach #database #multi #novel
A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases (LKS, OPV, UST, RV), pp. 620–629.
MLDMMLDM-2005-VermaVV #approach #using
Temporal Approach to Association Rule Mining Using T-Tree and P-Tree (KV, OPV, RV), pp. 651–659.
MLDMMLDM-2005-XiaWZL #data mining #modelling #random
Mixture Random Effect Model Based Meta-analysis for Medical Data Mining (YX, SW, CZ, SL), pp. 630–640.
MLDMMLDM-2005-YeWWCHT #approach
An Integrated Approach for Mining Meta-rules (FY, JW, SW, HC, TH, LT), pp. 549–557.
SEKESEKE-2005-BogornyEA #data mining #framework
A Reuse-based Spatial Data Preparation Framework for Data Mining (VB, PME, LOA), pp. 649–652.
SEKESEKE-2005-ChanS #data mining #rule-based
From Data to Knowledge: an Integrated Rule-Based Data Mining System (CCC, ZS), pp. 508–513.
SEKESEKE-2005-HuangLF #automation #email #query
A Chinese Text Mining Application: An Automatic Answer Reply to Customers’ E-mail Queries Model (JYH, HML, CLF), pp. 504–507.
SEKESEKE-2005-LoYT #algorithm #predict
Weighted Binary Sequential Mining Algorithm with Application to the Next-Day Appearance Prediction (SL, JY, FCT), pp. 783–782.
SIGIRSIGIR-2005-MagalhaesR #concept #incremental #information management #multi
Mining multimedia salient concepts for incremental information extraction (JM, SMR), pp. 641–642.
SIGIRSIGIR-2005-SehgalS #named
Manjal: a text mining system for MEDLINE (AKS, PS), p. 680.
SIGIRSIGIR-2005-SherfeseeO #framework #platform #research #web
A web mining research platform (DS, NO), p. 682.
SIGIRSIGIR-2005-ZhangHV #query #web
Mining translations of OOV terms from the web through cross-lingual query expansion (YZ, FH, SV), pp. 669–670.
PLDIPLDI-2005-MandelinXBK #api
Jungloid mining: helping to navigate the API jungle (DM, LX, RB, DK), pp. 48–61.
ASEASE-2005-DenmatDR #data mining #execution
Data mining and cross-checking of execution traces: a re-interpretation of Jones, Harrold and Stasko test information (TD, MD, OR), pp. 396–399.
ASEASE-2005-ShepherdPPC #analysis #automation #framework #named
Timna: a framework for automatically combining aspect mining analyses (DCS, JP, LLP, MCC), pp. 184–193.
ESEC-FSEESEC-FSE-2005-LivshitsZ #fault #named
DynaMine: finding common error patterns by mining software revision histories (VBL, TZ), pp. 296–305.
SACSAC-2005-AgyemangBA #n-gram #using #web
Mining web content outliers using structure oriented weighting techniques and N-grams (MA, KB, RA), pp. 482–487.
SACSAC-2005-Casale #analysis #network #performance #web
Combining queueing networks and web usage mining techniques for web performance analysis (GC), pp. 1699–1703.
SACSAC-2005-Morimoto #transitive
Optimized transitive association rule: mining significant stopover between events (YM), pp. 543–544.
SACSAC-2005-RahalRPNPRV #biology #incremental #interactive
Incremental interactive mining of constrained association rules from biological annotation data with nominal features (IR, DR, AP, HN, WP, RR, WVG), pp. 123–127.
SACSAC-2005-ShangS #database #relational
Depth-first frequent itemset mining in relational databases (XS, KUS), pp. 1112–1117.
SACSAC-2005-SilvestriO #approximate #distributed
Distributed approximate mining of frequent patterns (CS, SO), pp. 529–536.
SACSAC-2005-XuMY #concept #database #information management #scalability
Mining concept associations for knowledge discovery in large textual databases (XX, MM, NY), pp. 549–550.
PPoPPPPoPP-2005-CongHHP #data mining #framework #parallel
A sampling-based framework for parallel data mining (SC, JH, JH, DAP), pp. 255–265.
TACASTACAS-2005-WeimerN #detection #fault #specification
Mining Temporal Specifications for Error Detection (WW, GCN), pp. 461–476.
ICLPICLP-2005-Saidi #bound #process #using
Using CLP to Characterise Linguistic Lattice Boundaries in a Text Mining Process (ASS), pp. 418–420.
WICSAWICSA-2004-ZhuBJ #architecture #evaluation
Mining Patterns to Support Software Architecture Evaluation (LZ, MAB, DRJ), pp. 25–36.
JCDLJCDL-2004-LamCH #online
Mining events and new name translations from online daily news (WL, PSC, RH), pp. 287–295.
SIGMODSIGMOD-2004-AndritsosMT #database #scalability #set #tool support
Information-Theoretic Tools for Mining Database Structure from Large Data Sets (PA, RJM, PT), pp. 731–742.
SIGMODSIGMOD-2004-CaiCPHWA #data type #named
MAIDS: Mining Alarming Incidents from Data Streams (YDC, DC, GP, JH, MW, LA), pp. 919–920.
SIGMODSIGMOD-2004-Faloutsos
Indexing and Mining Streams (CF), p. 969.
VLDBVLDB-2004-AgrawalS #data mining #question
Whither Data Mining? (RA, RS), p. 9.
VLDBVLDB-2004-JiangPZ #interactive #named
GPX: Interactive Mining of Gene Expression Data (DJ, JP, AZ), pp. 1249–1252.
VLDBVLDB-2004-LinKLLN #database #monitoring #named #visual notation
VizTree: a Tool for Visually Mining and Monitoring Massive Time Series Databases (JL, EJK, SL, JPL, DMN), pp. 1269–1272.
VLDBVLDB-2004-XuCOTH #analysis #semantics
Semantic Mining and Analysis of Gene Expression Data (XX, GC, BCO, KLT, AKHT), pp. 1261–1264.
VLDBVLDB-2004-YuCLZ #data type #transaction
False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams (JXY, ZC, HL, AZ), pp. 204–215.
IWPCIWPC-2004-KanellopoulosT #c++ #clustering #comprehension #data mining #source code
Data Mining Source Code to Facilitate Program Comprehension: Experiments on Clustering Data Retrieved from C++ Programs (YK, CT), pp. 214–225.
SCAMSCAM-2004-TourweM #concept analysis #using
Mining Aspectual Views using Formal Concept Analysis (TT, KM), pp. 97–106.
WCREWCRE-2004-TonellaC #concept analysis #execution
Aspect Mining through the Formal Concept Analysis of Execution Traces (PT, MC), pp. 112–121.
ICEISICEIS-v1-2004-BouchouCAJL #named #xml
XRM: An XML-Based Language for Rule Mining Systems (BB, AC, MHFA, TYJ, DL), pp. 441–446.
ICEISICEIS-v1-2004-El-SonbatyK #algorithm #incremental #performance
New Fast Algorithm for Incremental Mining of Association Rules (YES, RK), pp. 275–281.
ICEISICEIS-v2-2004-BangDHD #concept #data mining #effectiveness #framework #knowledge base #segmentation
Data Mining of CRM Knowledge Bases for Effective Market Segmentation: A Conceptual Framework (JB, ND, LH, RRD), pp. 335–342.
ICEISICEIS-v2-2004-DangerRB #approach #named
Objectminer: A New Approach for Mining Complex Objects (RD, JRS, RBL), pp. 42–47.
ICEISICEIS-v2-2004-DoP #data mining #tool support #towards #visualisation
Towards High Dimensional Data Mining with Boosting of PSVM and Visualization Tools (TND, FP), pp. 36–41.
ICEISICEIS-v2-2004-DykC #education #process #statistics
Warehousing and Mining of Higher Education Data by Means of Statistical Process Control (LVD, PC), pp. 110–115.
ICEISICEIS-v2-2004-HamzaY #constraints #regular expression #using
Mining Sequential Patterns with Regular Expression Constraints Using Sequential Pattern Tree (MH, KM, MY), pp. 116–121.
ICEISICEIS-v2-2004-HuysmansMVB #web
Web Usage Mining with Time Constrained Association Rules (JH, CM, JV, BB), pp. 343–348.
ICEISICEIS-v2-2004-KooptiwootS #set #using
Mining the Relationships in the Form of the Predisposing Factors and Coincident Factors Among Numerical Dynamic Attributes in Time Series Data Set by Using the Combination of Some Existing Techniques (SK, MAS), pp. 327–334.
ICEISICEIS-v2-2004-KooptiwootS04a #idea #set #using
Mining the Relationships in the Form of Predisposing Factor and Coincident Factor in Time Series Data Set by Using the Combination of some Existing Ideas with a new Idea from the Fact in the Chemical Reaction (SK, MAS), pp. 531–534.
ICEISICEIS-v2-2004-KuusikLV #clique #data mining
Data Mining: Pattern Mining as a Clique Extracting Task (RK, GL, LV), pp. 519–522.
ICEISICEIS-v2-2004-MataRR #case study #data mining #development
Applying Data Mining to Software Development Projects: A Case Study (JMV, JLÁM, JCRS, IR), pp. 54–60.
ICEISICEIS-v2-2004-Poulet #data mining #towards #visual notation
Towards Visual Data Mining (FP), pp. 349–356.
ICEISICEIS-v2-2004-TagarelliTG #clustering #linear #programming
Mining Scientific Results Through the Combined Use of Clustering and Linear Programming Techniques (AT, IT, SG), pp. 84–91.
ICEISICEIS-v2-2004-UdechukwuBA #framework #performance
An Efficient Framework for Iterative Time-Series Trend Mining (AU, KB, RA), pp. 130–137.
ICEISICEIS-v2-2004-ZarateAPR #data mining
Data Mining Application to Obtain Profiles of Patients with Nephrolithiasis (LEZ, PA, RP, TR), pp. 104–109.
ICEISICEIS-v4-2004-LimaEMFC #data mining #information management #web
Archcollect Front-End: A Web Usage Data Mining Knowledge Acquisition Mechanism Focused on Static or Dynamic Contenting Applications (JdCL, AAAE, JGdM, BF, TGdSC), pp. 258–262.
ICEISICEIS-v5-2004-BadjioP #data mining #tool support #usability #visual notation
Usability of Visual Data Mining Tools (EPFB, FP), pp. 254–258.
ICEISICEIS-v5-2004-JantkeLGGTT #data mining #learning
Learning by Doing and Learning when Doing: Dovetailing E-Learning and Decision Support with a Data Mining Tutor (KPJ, SL, GG, PAG, BT, BT), pp. 238–241.
CIKMCIKM-2004-ChungJM #clustering #dataset #using
Mining gene expression datasets using density-based clustering (SC, JJ, DM), pp. 150–151.
CIKMCIKM-2004-Ramakrishnan
The EDAM project: mining mass spectra and more (RR), p. 1.
CIKMCIKM-2004-WangXY #biology #scalability #sequence
Scalable sequential pattern mining for biological sequences (KW, YX, JXY), pp. 178–187.
ICMLICML-2004-NatteeSNO #first-order #learning #multi
Learning first-order rules from data with multiple parts: applications on mining chemical compound data (CN, SS, MN, TO).
ICPRICPR-v2-2004-BouchaffraT #concept #design
Introduction to the Concept of Structural HMM: Application to Mining Customers’ Preferences in Automotive Design (DB, JT), pp. 493–496.
KDDKDD-2004-AbajoDLC #case study #data mining #delivery #industrial #modelling #quality
ANN quality diagnostic models for packaging manufacturing: an industrial data mining case study (NdA, ABD, VL, SRC), pp. 799–804.
KDDKDD-2004-AgichteinG #automation #segmentation
Mining reference tables for automatic text segmentation (EA, VG), pp. 20–29.
KDDKDD-2004-AksoyKTM #classification #image #interactive
Interactive training of advanced classifiers for mining remote sensing image archives (SA, KK, CT, GBM), pp. 773–782.
KDDKDD-2004-CaruanaN #analysis #data mining #empirical #learning #metric #performance
Data mining in metric space: an empirical analysis of supervised learning performance criteria (RC, ANM), pp. 69–78.
KDDKDD-2004-ChengYH #database #incremental #named #scalability
IncSpan: incremental mining of sequential patterns in large database (HC, XY, JH), pp. 527–532.
KDDKDD-2004-DavidsonGST #algorithm #approach #data mining #matrix #quality
A general approach to incorporate data quality matrices into data mining algorithms (ID, AG, AS, GKT), pp. 794–798.
KDDKDD-2004-EsterGJH #data mining #problem #segmentation
A microeconomic data mining problem: customer-oriented catalog segmentation (ME, RG, WJ, ZH), pp. 557–562.
KDDKDD-2004-GadeWK #constraints #performance
Efficient closed pattern mining in the presence of tough block constraints (KG, JW, GK), pp. 138–147.
KDDKDD-2004-HeCH #approach #correlation #interface #query #web
Discovering complex matchings across web query interfaces: a correlation mining approach (BH, KCCC, JH), pp. 148–157.
KDDKDD-2004-Heckerman #data mining #modelling #visual notation
Graphical models for data mining (DH), p. 2.
KDDKDD-2004-HorvathGW #graph #kernel #predict
Cyclic pattern kernels for predictive graph mining (TH, TG, SW), pp. 158–167.
KDDKDD-2004-HuanWPY #database #graph #named
SPIN: mining maximal frequent subgraphs from graph databases (JH, WW, JP, JY), pp. 581–586.
KDDKDD-2004-HuL
Mining and summarizing customer reviews (MH, BL), pp. 168–177.
KDDKDD-2004-JehW #graph
Mining the space of graph properties (GJ, JW), pp. 187–196.
KDDKDD-2004-JiangPRTZ #array #clustering
Mining coherent gene clusters from gene-sample-time microarray data (DJ, JP, MR, CT, AZ), pp. 430–439.
KDDKDD-2004-JinZM #analysis #probability #semantics #web
Web usage mining based on probabilistic latent semantic analysis (XJ, YZ, BM), pp. 197–205.
KDDKDD-2004-KantarciogluJC #data mining #privacy #question
When do data mining results violate privacy? (MK, JJ, CC), pp. 599–604.
KDDKDD-2004-KeoghLR #data mining #towards
Towards parameter-free data mining (EJK, SL, C(R), pp. 206–215.
KDDKDD-2004-LinKLLN #monitoring #visual notation
Visually mining and monitoring massive time series (JL, EJK, SL, JPL, DMN), pp. 460–469.
KDDKDD-2004-MamoulisCKHTC #query
Mining, indexing, and querying historical spatiotemporal data (NM, HC, GK, MH, YT, DWC), pp. 236–245.
KDDKDD-2004-NakataT #predict
Mining traffic data from probe-car system for travel time prediction (TN, JiT), pp. 817–822.
KDDKDD-2004-NatsevNS #multi #representation #semantics
Semantic representation: search and mining of multimedia content (AN, MRN, JRS), pp. 641–646.
KDDKDD-2004-NijssenK #difference
A quickstart in frequent structure mining can make a difference (SN, JNK), pp. 647–652.
KDDKDD-2004-RamakrishnanKMPH #algorithm
Turning CARTwheels: an alternating algorithm for mining redescriptions (NR, DK, BM, MP, RFH), pp. 266–275.
KDDKDD-2004-WangWPZS #database #graph #scalability
Scalable mining of large disk-based graph databases (CW, WW, JP, YZ, BS), pp. 316–325.
KDDKDD-2004-WuGH #clustering #network #using
Mining scale-free networks using geodesic clustering (AYW, MG, JH), pp. 719–724.
KDDKDD-2004-Yang #complexity
The complexity of mining maximal frequent itemsets and maximal frequent patterns (GY), pp. 344–353.
KDDKDD-2004-YangLHG #named #performance #query #xml
2PXMiner: an efficient two pass mining of frequent XML query patterns (LHY, MLL, WH, XG), pp. 731–736.
KDDKDD-2004-ZhaiVY #comparative
A cross-collection mixture model for comparative text mining (CZ, AV, BY), pp. 743–748.
KDDKDD-2004-ZhangMCS #performance
Fast mining of spatial collocations (XZ, NM, DWC, YS), pp. 384–393.
KDDKDD-2004-ZhangZK #approach #category theory #data mining #image #modelling
A data mining approach to modeling relationships among categories in image collection (RZ, Z(Z, SK), pp. 749–754.
KDDKDD-2004-ZhuL #data mining #privacy
Optimal randomization for privacy preserving data mining (MYZ, LL), pp. 761–766.
SIGIRSIGIR-2004-LamHC #learning #similarity
Learning phonetic similarity for matching named entity translations and mining new translations (WL, RH, PSC), pp. 289–296.
SIGIRSIGIR-2004-XuU #analysis #ranking #web
A unified model of literal mining and link analysis for ranking web resources (YX, KU), pp. 546–547.
ASEASE-2004-BreuK #using
Aspect Mining Using Event Traces (SB, JK), pp. 310–315.
ICSEICSE-2004-ZimmermannWDZ #version control
Mining Version Histories to Guide Software Changes (TZ, PW, SD, AZ), pp. 563–572.
SACSAC-2004-AgyemangBA #framework #web
Framework for mining web content outliers (MA, KB, RA), pp. 590–594.
SACSAC-2004-Goethals #memory management
Memory issues in frequent itemset mining (BG), pp. 530–534.
SACSAC-2004-HuP #approach #data mining #database #detection
A data mining approach for database intrusion detection (YH, BP), pp. 711–716.
SACSAC-2004-KusumuraHN
Text mining agent for net auction (YK, YH, SN), pp. 1095–1102.
SACSAC-2004-OrlandoPS #algorithm #sequence
A new algorithm for gap constrained sequence mining (SO, RP, CS), pp. 540–547.
SACSAC-2004-QiW #set
A model for mining outliers from complex data sets (HQ, JW), pp. 595–599.
SACSAC-2004-Savinov #dependence
Mining dependence rules by finding largest itemset support quota (AAS), pp. 525–529.
SACSAC-2004-ShangSG #generative #sql
SQL based frequent pattern mining without candidate generation (XS, KUS, IG), pp. 618–619.
HPDCHPDC-2004-GilburdSW #data mining #privacy
Privacy-Preserving Data Mining on Data Grids in the Presence of Malicious Participants (BG, AS, RW), pp. 225–234.
PDPPDP-2004-BorzemskiLN #analysis #data mining #internet #performance
Application of Data Mining for the Analysis of Internet Path Performance (LB, LL, ZN), pp. 54–59.
DRRDRR-2003-YangL #image #retrieval #semantics
Semantics-based image retrieval by text mining on environmental texts (HCY, CHL), pp. 266–277.
JCDLJCDL-2003-LeroyCMEFKHLXMN #data mining #named
Genescene: Biomedical Text And Data Mining (GL, HC, JDM, SE, RRF, KLK, ZH, JL, JJX, DM, TGN), pp. 116–118.
PODSPODS-2003-EvfimievskiGS #data mining #privacy
Limiting privacy breaches in privacy preserving data mining (AVE, JG, RS), pp. 211–222.
PODSPODS-2003-RameshMZ #data mining #theory and practice
Feasible itemset distributions in data mining: theory and application (GR, WM, MJZ), pp. 284–295.
VLDBVLDB-2003-HinneburgLH #data mining #database #named
COMBI-Operator: Database Support for Data Mining Applications (AH, WL, DH), pp. 429–439.
VLDBVLDB-2003-NieKH #effectiveness #integration #named #statistics #using
BibFinder/StatMiner: Effectively Mining and Using Coverage and Overlap Statistics in Data Integration (ZN, SK, TH), pp. 1097–1100.
VLDBVLDB-2003-PapadimitriouBF #adaptation
Adaptive, Hands-Off Stream Mining (SP, AB, CF), pp. 560–571.
VLDBVLDB-2003-TengCY #data type
A Regression-Based Temporal Pattern Mining Scheme for Data Streams (WGT, MSC, PSY), pp. 93–104.
VLDBVLDB-2003-WangZL #data mining #data type #named #sql
ATLAS: A Small but Complete SQL Extension for Data Mining and Data Streams (HW, CZ, CL), pp. 1113–1116.
VLDBVLDB-2003-YangLH #performance #query #xml
Efficient Mining of XML Query Patterns for Caching (LHY, MLL, WH), pp. 69–80.
ICSMEICSM-2003-BalanyiF #c++ #design pattern #source code
Mining Design Patterns from C++ Source Code (ZB, RF), pp. 305–314.
ICSMEICSM-2003-Sayyad-ShirabadLM #legacy #maintenance
Mining the Maintenance History of a Legacy Software System (JSS, TCL, SM), pp. 95–104.
IWPCIWPC-2003-TjortjisSL #comprehension #source code
Facilitating Program Comprehension by Mining Association Rules from Source Code (CT, LS, PJL), pp. 125–133.
ICEISICEIS-v2-2003-DoP #algorithm #dataset #scalability
Mining Very Large Datasets with Support Vector Machine Algorithms (TND, FP), pp. 140–147.
ICEISICEIS-v2-2003-FernedaPS
Text Mining for Organizational Intelligence (EF, HAdP, EMS), pp. 446–450.
ICEISICEIS-v2-2003-GalianoCMSB #data mining #usability
Usability Issues in Data Mining Systems (FBG, JCC, NM, JMS, IJB), pp. 418–421.
ICEISICEIS-v2-2003-KrolikowskiMP #data mining #query
Set-Oriented Indexes for Data Mining Queries (ZK, MM, JP), pp. 316–323.
ICEISICEIS-v2-2003-KuusikL #approach #data mining #using
An Approach of Data Mining Using Monotone Systems (RK, GL), pp. 482–485.
ICEISICEIS-v2-2003-MaciasVSR #data mining #development
A Data Mining Method to Support Decision Making in Software Development Projects (JLÁM, JMV, JCRS, IR), pp. 11–18.
ICEISICEIS-v2-2003-PetitP #approach #data mining
A New Approach of Data Mining: The Meta Projectories (CP, SP), pp. 515–518.
ICEISICEIS-v2-2003-SaravananR #summary
Text Summarization: An Upcoming Tool in Text Mining (MS, SR), pp. 541–544.
ICEISICEIS-v2-2003-SrikumarB #algorithm #set #transaction
An Algorithm for Mining Maximal Frequent Sets Based on Dominancy of Transactions (KS, BB), pp. 422–425.
ICEISICEIS-v2-2003-WangRLC #online #realtime #recommendation #web
Mining Web Usage Data for Real-Time Online Recommendation (MW, SJR, SYL, JKYC), pp. 575–578.
ICEISICEIS-v3-2003-HoangHB #data mining #detection
Intrusion Detection Based on Data Mining (XDH, JH, PB), pp. 341–346.
CIKMCIKM-2003-LinK #using #web
Question answering from the web using knowledge annotation and knowledge mining techniques (JJL, BK), pp. 116–123.
CIKMCIKM-2003-SunL #web
Web unit mining: finding and classifying subgraphs of web pages (AS, EPL), pp. 108–115.
CIKMCIKM-2003-TangZ #multi
Mining multiple phenotype structures underlying gene expression profiles (CT, AZ), pp. 418–425.
KDDKDD-2003-Aggarwal #data mining #design #distance #towards
Towards systematic design of distance functions for data mining applications (CCA), pp. 9–18.
KDDKDD-2003-ArgamonSS #multi
Style mining of electronic messages for multiple authorship discrimination: first results (SA, MS, SSS), pp. 475–480.
KDDKDD-2003-BayS #linear
Mining distance-based outliers in near linear time with randomization and a simple pruning rule (SDB, MS), pp. 29–38.
KDDKDD-2003-BhatnagarKN #classification
Mining high dimensional data for classifier knowledge (RB, GK, WN), pp. 481–486.
KDDKDD-2003-DuZ #data mining #privacy #random #using
Using randomized response techniques for privacy-preserving data mining (WD, JZZ), pp. 505–510.
KDDKDD-2003-El-HajjZ #dataset #interactive #matrix #performance #scalability
Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining (MEH, ORZ), pp. 109–118.
KDDKDD-2003-EtzioniTKY
To buy or not to buy: mining airfare data to minimize ticket purchase price (OE, RT, CAK, AY), pp. 119–128.
KDDKDD-2003-FramAD #data mining #empirical #safety
Empirical Bayesian data mining for discovering patterns in post-marketing drug safety (DMF, JSA, WD), pp. 359–368.
KDDKDD-2003-GamaRM #data type #performance
Accurate decision trees for mining high-speed data streams (JG, RR, PM), pp. 523–528.
KDDKDD-2003-HoNKLNYT #abstraction
Mining hepatitis data with temporal abstraction (TBH, TDN, SK, SQL, DN, HY, KT), pp. 369–377.
KDDKDD-2003-HsuDL #database #image
Mining viewpoint patterns in image databases (WH, JD, MLL), pp. 553–558.
KDDKDD-2003-LastFK #approach #automation #data mining #testing
The data mining approach to automated software testing (ML, MF, AK), pp. 388–396.
KDDKDD-2003-LiuGZ #web
Mining data records in Web pages (BL, RLG, YZ), pp. 601–606.
KDDKDD-2003-MorinagaYT #distributed
Distributed cooperative mining for information consortia (SM, KY, JiT), pp. 619–624.
KDDKDD-2003-SequeiraZSC #data mining #locality #source code
Improving spatial locality of programs via data mining (KS, MJZ, BKS, CDC), pp. 649–654.
KDDKDD-2003-TangZP
Mining phenotypes and informative genes from gene expression data (CT, AZ, JP), pp. 655–660.
KDDKDD-2003-TaoMF #framework #using
Weighted Association Rule Mining using weighted support and significance framework (FT, FM, MMF), pp. 661–666.
KDDKDD-2003-WangFYH #classification #concept #data type #using
Mining concept-drifting data streams using ensemble classifiers (HW, WF, PSY, JH), pp. 226–235.
KDDKDD-2003-WangHP
CLOSET+: searching for the best strategies for mining frequent closed itemsets (JW, JH, JP), pp. 236–245.
KDDKDD-2003-WangJL
Mining unexpected rules by pushing user dynamics (KW, YJ, LVSL), pp. 246–255.
KDDKDD-2003-WeissBKD #data mining #knowledge-based
Knowledge-based data mining (SMW, SJB, SK, SD), pp. 456–461.
KDDKDD-2003-YanH #graph #named
CloseGraph: mining closed frequent graph patterns (XY, JH), pp. 286–295.
KDDKDD-2003-YiLL #data mining #web
Eliminating noisy information in Web pages for data mining (LY, BL, XL), pp. 296–305.
KDDKDD-2003-ZakiG #performance #using
Fast vertical mining using diffsets (MJZ, KG), pp. 326–335.
KDDKDD-2003-ZhangSY #data mining
Applying data mining in investigating money laundering crimes (Z(Z, JJS, PSY), pp. 747–752.
MLDMMLDM-2003-AntunesO #constraints
Generalization of Pattern-Growth Methods for Sequential Pattern Mining with Gap Constraints (CA, ALO), pp. 239–251.
MLDMMLDM-2003-Bunke #data mining #graph #machine learning #tool support
Graph-Based Tools for Data Mining and Machine Learning (HB), pp. 7–19.
MLDMMLDM-2003-ChiuX #optimisation
Optimizing Financial Portfolios from the Perspective of Mining Temporal Structures of Stock Returns (KCC, LX), pp. 266–275.
MLDMMLDM-2003-KayaA
Integrating Fuzziness with OLAP Association Rules Mining (MK, RA), pp. 353–368.
MLDMMLDM-2003-LeleuRBE #dataset #named
GO-SPADE: Mining Sequential Patterns over Datasets with Consecutive Repetitions (ML, CR, JFB, GE), pp. 293–306.
SEKESEKE-2003-ChapinK #data mining #metric #re-engineering
Validative measurement in software engineering: a data mining example (NC, MKM), pp. 626–633.
SEKESEKE-2003-LaiLB #data access #database #object-oriented
Mining Access Patterns for Enhancing Navigational Access in Object-Oriented Database (CL, SL, JB), pp. 679–684.
SEKESEKE-2003-LaiSF #realtime
Real-Time Mining of Partial Periodic Patterns (CL, LS, MF), pp. 259–266.
SEKESEKE-2003-WuS #approach #data mining
A Data Mining Approach for Dynamic Software Project Plan Tracking (CSW, DBS), pp. 634–638.
PADLPADL-2003-ClareK #data mining #functional #lazy evaluation
Data Mining the Yeast Genome in a Lazy Functional Language (AC, RDK), pp. 19–36.
PADLPADL-2003-Page #biology #database #declarative
The Role of Declarative Languages in Mining Biological Databases (CDPJ), p. 1.
SACSAC-2003-BarbaraLLJC #data mining #detection
Bootstrapping a Data Mining Intrusion Detection System (DB, YL, JLL, SJ, JC), pp. 421–425.
SACSAC-2003-Ferrer-TroyanoAS #data type #prototype
Prototype-based Mining of Numeric Data Streams (FJFT, JSAR, JCRS), pp. 480–484.
SACSAC-2003-HuaJVT #algebra #analysis #approach #data mining #named #performance #using
ADMiRe: An Algebraic Approach to System Performance Analysis Using Data Mining Techniques (KAH, NJ, RV, DAT), pp. 490–496.
SACSAC-2003-HuangXSP
Mining Confident Colocation Rules without A Support Threshold (YH, HX, SS, JP), pp. 497–501.
SACSAC-2003-JiaPP #constraints
Tough Constraint-Based Frequent Closed Itemsets Mining (LJ, RP, DP), pp. 416–420.
SACSAC-2003-Lin
Mining Maximal Frequent Intervals (JLL), pp. 426–431.
SACSAC-2003-LiZO #data mining #distributed #modelling #similarity
A New Distributed Data Mining Model Based on Similarity (TL, SZ, MO), pp. 432–436.
SACSAC-2003-Meo #data mining #optimisation
Optimization of a Language for Data Mining (RM), pp. 437–444.
SACSAC-2003-NenadicSA
Terminology-driven Mining of Biomedical Literature (GN, IS, SA), pp. 83–87.
SACSAC-2003-TsengT #mobile #performance #web
An Efficient Method for Mining Associated Service Patterns in Mobile Web Environments (SMT, CFT), pp. 455–459.
PPoPPPPoPP-2003-Kazar #data mining #performance #scalability
High performance spatial data mining for very large data-sets (BMK), p. 1.
SIGMODSIGMOD-2002-DasuJMS #database #how #quality
Mining database structure; or, how to build a data quality browser (TD, TJ, SM, VS), pp. 240–251.
SIGMODSIGMOD-2002-GarofalakisGR #data type #query #tutorial
Querying and mining data streams: you only get one look a tutorial (MNG, JG, RR), p. 635.
SIGMODSIGMOD-2002-YangWYH
Mining long sequential patterns in a noisy environment (JY, WW, PSY, JH), pp. 406–417.
VLDBVLDB-2002-Chakrabarti #fine-grained
Searching and Mining Fine-Grained Semi-Structured Data (SC).
VLDBVLDB-2002-Faloutsos #analysis #data mining #similarity
Sensor Data Mining: Similarity Search and Pattern Analysis (CF).
VLDBVLDB-2002-GarofalakisG #data type #query
Querying and Mining Data Streams: You Only Get One Look (MNG, JG).
VLDBVLDB-2002-OhuraTPK #internet #query #using #web
Experiments on Query Expansion for Internet Yellow Page Services Using Web Log Mining (YO, KT, IP, MK), pp. 1008–1018.
VLDBVLDB-2002-RizviH #maintenance #privacy
Maintaining Data Privacy in Association Rule Mining (SR, JRH), pp. 682–693.
IWPCIWPC-2002-El-RamlySS #case study #interactive #modelling
Mining System-User Interaction Traces for Use Case Models (MER, ES, PGS), pp. 21–29.
ICALPICALP-2002-Mannila #data mining #problem
Local and Global Methods in Data Mining: Basic Techniques and Open Problems (HM), pp. 57–68.
ICEISICEIS-2002-CarrascoMG #data mining #flexibility #named #query
FSQL: A Flexible Query Language for Data Mining (RAC, MAVM, JG), pp. 50–56.
ICEISICEIS-2002-KrishnaswamyLZ #data mining #distributed #optimisation #predict #runtime
Supporting the Optimisation of Distributed Data Mining by Predicting Application Run Times (SK, SWL, ABZ), pp. 374–381.
ICEISICEIS-2002-LiK #e-commerce #framework
Intelligent Agent-Based Framework for Mining Customer Buying Habits in E-Commerce (QL, RK), pp. 1016–1022.
ICEISICEIS-2002-MengYCC #data mining #information management
Data Mining Mechanisms in Knowledge Management System (IHM, WPY, WCC, LPC), pp. 399–404.
ICEISICEIS-2002-SanchezSVACD #data mining #using
Using Data Mining Techniques to Analyze Correspondences between Partitions (DS, JMS, MAVM, VA, JC, GD), pp. 179–186.
ICEISICEIS-2002-SantosNASR #classification #data mining #database #learning #using
Augmented Data Mining over Clinical Databases Using Learning Classifier Systems (MFS, JN, AA, ÁMS, FR), pp. 512–516.
ICEISICEIS-2002-TanTS #data mining #parallel #performance #taxonomy
A Taxonomy for Inter-Model Parallelism in High Performance Data Mining (LT, DT, KAS), pp. 534–539.
CIKMCIKM-2002-Faloutsos #data mining #network #self
Future directions in data mining: streams, networks, self-similarity and power laws (CF), p. 93.
CIKMCIKM-2002-KaoCLH #analysis #web
Entropy-based link analysis for mining web informative structures (HYK, MSC, SHL, JMH), pp. 574–581.
CIKMCIKM-2002-MotoyoshiMW
Mining temporal classes from time series data (MM, TM, KW), pp. 493–498.
CIKMCIKM-2002-NahmM
Mining soft-matching association rules (UYN, RJM), pp. 681–683.
CIKMCIKM-2002-NieNVK #statistics
Mining coverage statistics for websource selection in a mediator (ZN, UN, SV, SK), pp. 678–680.
CIKMCIKM-2002-PanF #data mining #library #quote #video
“GeoPlot”: spatial data mining on video libraries (JYP, CF), pp. 405–412.
CIKMCIKM-2002-PeiHW #constraints #database #scalability
Mining sequential patterns with constraints in large databases (JP, JH, WW), pp. 18–25.
ICMLICML-2002-WuZZ
Mining Both Positive and Negative Association Rules (XW, CZ, SZ), pp. 658–665.
ICPRICPR-v3-2002-HongH #multimodal
Multimodal Temporal Pattern Mining (PH, TSH), pp. 465–472.
KDDKDD-2002-Aggarwal02a #case study #collaboration #crawling #experience #resource management #topic #user interface
Collaborative crawling: mining user experiences for topical resource discovery (CCA), pp. 423–428.
KDDKDD-2002-AlqallafKMZ #correlation #data mining #robust #scalability
Scalable robust covariance and correlation estimates for data mining (FAA, KPK, RDM, RHZ), pp. 14–23.
KDDKDD-2002-AyresFGY #representation #using
Sequential PAttern mining using a bitmap representation (JA, JF, JG, TY), pp. 429–435.
KDDKDD-2002-El-RamlySS #approach #behaviour #runtime
From run-time behavior to usage scenarios: an interaction-pattern mining approach (MER, ES, PGS), pp. 315–324.
KDDKDD-2002-EsterKS #web
Web site mining: a new way to spot competitors, customers and suppliers in the world wide web (ME, HPK, MS), pp. 249–258.
KDDKDD-2002-EvfimievskiSAG #privacy
Privacy preserving mining of association rules (AVE, RS, RA, JG), pp. 217–228.
KDDKDD-2002-HultenD #constant #database #modelling #scalability
Mining complex models from arbitrarily large databases in constant time (GH, PMD), pp. 525–531.
KDDKDD-2002-JulischD #detection
Mining intrusion detection alarms for actionable knowledge (KJ, MD), pp. 366–375.
KDDKDD-2002-KeoghK #benchmark #data mining #empirical #metric #on the #overview
On the need for time series data mining benchmarks: a survey and empirical demonstration (EJK, SK), pp. 102–111.
KDDKDD-2002-LiangK #network
Mining heterogeneous gene expression data with time lagged recurrent neural networks (YL, AK), pp. 415–421.
KDDKDD-2002-LinLCY #data mining #database #distributed #transaction
Distributed data mining in a chain store database of short transactions (CRL, CHL, MSC, PSY), pp. 576–581.
KDDKDD-2002-LittleJLRS #data mining
Collusion in the U.S. crop insurance program: applied data mining (BBL, WLJ, ACL, RMR, SAS), pp. 594–598.
KDDKDD-2002-LiuL #adaptation #classification #documentation #incremental
Incremental context mining for adaptive document classification (RLL, YLL), pp. 599–604.
KDDKDD-2002-LiuPWH #set
Mining frequent item sets by opportunistic projection (JL, YP, KW, JH), pp. 229–238.
KDDKDD-2002-MorinagaYTF #web
Mining product reputations on the Web (SM, KY, KT, TF), pp. 341–349.
KDDKDD-2002-PalmerGF #data mining #graph #named #performance #scalability
ANF: a fast and scalable tool for data mining in massive graphs (CRP, PBG, CF), pp. 81–90.
KDDKDD-2002-RichardsonD
Mining knowledge-sharing sites for viral marketing (MR, PMD), pp. 61–70.
KDDKDD-2002-SequeiraZ #data mining #named
ADMIT: anomaly-based data mining for intrusions (KS, MJZ), pp. 386–395.
KDDKDD-2002-VaidyaC #privacy
Privacy preserving association rule mining in vertically partitioned data (JV, CC), pp. 639–644.
KDDKDD-2002-WuFS #approach #classification #data mining #named
B-EM: a classifier incorporating bootstrap with EM approach for data mining (XW, JF, KRS), pp. 670–675.
KDDKDD-2002-Zaki
Efficiently mining frequent trees in a forest (MJZ), pp. 71–80.
SEKESEKE-2002-PoleseTT #data mining
A data mining based system supporting tactical decisions (GP, MT, GT), pp. 681–684.
SIGIRSIGIR-2002-ChoudhariDJMMPR #named #ontology #web
YellowPager: a tool for ontology-based mining of service directories from web sources (PC, HD, AJ, AM, SM, SP, IVR), p. 458.
SPLCSPLC-2002-SmithBB #analysis #component #product line #re-engineering #using
Using the Options Analysis for Reengineering (OAR) Method for Mining Components for a Product Line (DBS, LO, JB), pp. 316–327.
POPLPOPL-2002-AmmonsBL #specification
Mining specifications (GA, RB, JRL), pp. 4–16.
SACSAC-2002-ChangJ #clustering #data mining #scalability
A new cell-based clustering method for large, high-dimensional data in data mining applications (JWC, DSJ), pp. 503–507.
SACSAC-2002-Geist #data mining #framework
A framework for data mining and KDD (IG), pp. 508–513.
SACSAC-2002-KrishnaswamyLZ #data mining #estimation #metric #quality #runtime
Application run time estimation: a quality of service metric for web-based data mining services (SK, SWL, ABZ), pp. 1153–1159.
SACSAC-2002-MaciasVR #algorithm #using
Mining interesting regions using an evolutionary algorithm (JLÁM, JMV, JCRS), pp. 498–502.
SACSAC-2002-WangW #approach #data mining #optimisation #query #relational
Optimizing relational store for e-catalog queries: a data mining approach (MW, XSW), pp. 1147–1152.
DACDAC-2002-LiuSRC #data mining #design #megamodelling #scalability
Remembrance of circuits past: macromodeling by data mining in large analog design spaces (HL, AS, RAR, LRC), pp. 437–442.
WICSAWICSA-2001-StoermerO #architecture #named #product line
MAP — Mining Architectures for Product Line Evaluations (CS, LO), pp. 35–44.
JCDLJCDL-2001-PanFV #classification #named #video
VideoGraph: a new tool for video mining and classification (JYP, CF), pp. 116–117.
PODSPODS-2001-AgrawalA #algorithm #data mining #design #on the #privacy #quantifier
On the Design and Quantification of Privacy Preserving Data Mining Algorithms (DA, CCA).
VLDBVLDB-2001-DongHLPW #multi
Mining Multi-Dimensional Constrained Gradients in Data Cubes (GD, JH, JMWL, JP, KW), pp. 321–330.
VLDBVLDB-2001-MargaritisFT #data mining #named #performance #scalability
NetCube: A Scalable Tool for Fast Data Mining and Compression (DM, CF, ST), pp. 311–320.
VLDBVLDB-2001-SadriZZA #data mining #query
A Sequential Pattern Query Language for Supporting Instant Data Mining for e-Services (RS, CZ, AMZ, JA), pp. 653–656.
ICSMEICSM-2001-ShirabadLM #maintenance
Supporting Software Maintenance by Mining Software Update Records (JSS, TCL, SM), pp. 22–31.
ICEISICEIS-v1-2001-HippGG #algorithm #database #relational
Integrating Association Rule Mining Algorithms with Relational Database Systems (JH, UG, UG), pp. 130–137.
ICEISICEIS-v1-2001-ThorntonR #artificial reality #data mining #network #using
Using Virtual Reality Data Mining for Network Management (KEBT, CR), pp. 340–344.
CIKMCIKM-2001-DasNW #agile
Rapid Association Rule Mining (AD, WKN, YKW), pp. 474–481.
CIKMCIKM-2001-GhaniJM #corpus #web
Mining the Web to Create Minority Language Corpora (RG, RJ, DM), pp. 279–286.
CIKMCIKM-2001-KrishnaK #algorithm #clustering #symmetry
A Clustering Algorithm for Asymmetrically Related Data with Applications to Text Mining (KK, RK), pp. 571–573.
CIKMCIKM-2001-LeeLC #algorithm #incremental #performance
Sliding-Window Filtering: An Efficient Algorithm for Incremental Mining (CHL, CRL, MSC), pp. 263–270.
CIKMCIKM-2001-NanavatiCJK
Mining Generalised Disjunctive Association Rules (AAN, KPC, SJ, RK), pp. 482–489.
CIKMCIKM-2001-PintoHPWCD #multi
Multi-Dimensional Sequential Pattern Mining (HP, JH, JP, KW, QC, UD), pp. 81–88.
CIKMCIKM-2001-RadevQZBZFP #natural language #web
Mining the Web for Answers to Natural Language Questions (DRR, HQ, ZZ, SBG, ZZ, WF, JMP), pp. 143–150.
CIKMCIKM-2001-WangHCC
Mining Confident Rules Without Support Requirement (KW, YH, DWLC, FYLC), pp. 89–96.
KDDKDD-2001-AdderleyM #behaviour #case study #commit #data mining #modelling
Data mining case study: modeling the behavior of offenders who commit serious sexual assaults (RA, PBM), pp. 215–220.
KDDKDD-2001-AggarwalP #concept #re-engineering #semistructured data #set
Mining massively incomplete data sets by conceptual reconstruction (CCA, SP), pp. 227–232.
KDDKDD-2001-BiFK
The “DGX” distribution for mining massive, skewed data (ZB, CF, FK), pp. 17–26.
KDDKDD-2001-BujaL #classification #data mining
Data mining criteria for tree-based regression and classification (AB, YSL), pp. 27–36.
KDDKDD-2001-DittrichS #algorithm #named #scalability #set
GESS: a scalable similarity-join algorithm for mining large data sets in high dimensional spaces (JPD, BS), pp. 47–56.
KDDKDD-2001-DomingosR #network
Mining the network value of customers (PMD, MR), pp. 57–66.
KDDKDD-2001-Edelstein #data mining #question
Data mining: are we there yet? (HE), p. 7.
KDDKDD-2001-Elkan #challenge #data mining #lessons learnt
Magical thinking in data mining: lessons from CoIL challenge 2000 (CE), pp. 426–431.
KDDKDD-2001-GarckeG #data mining #using
Data mining with sparse grids using simplicial basis functions (JG, MG), pp. 87–96.
KDDKDD-2001-HueglinV #data mining
Data mining techniques to improve forecast accuracy in airline business (CH, FV), pp. 438–442.
KDDKDD-2001-HultenSD #data type
Mining time-changing data streams (GH, LS, PMD), pp. 97–106.
KDDKDD-2001-JinTH #database #scalability
Mining top-n local outliers in large databases (WJ, AKHT, JH), pp. 293–298.
KDDKDD-2001-Kohavi #e-commerce
Mining e-commerce data: the good, the bad, and the ugly (RK), pp. 8–13.
KDDKDD-2001-KramerRH
Molecular feature mining in HIV data (SK, LDR, CH), pp. 136–143.
KDDKDD-2001-LambertP #transaction
Mining a stream of transactions for customer patterns (DL, JCP), pp. 305–310.
KDDKDD-2001-LiY
Mining from open answers in questionnaire data (HL, KY), pp. 443–449.
KDDKDD-2001-MahHL #network
Funnel report mining for the MSN network (TM, HH, YL), pp. 450–455.
KDDKDD-2001-Morimoto #database #set
Mining frequent neighboring class sets in spatial databases (YM), pp. 353–358.
KDDKDD-2001-Netz #data mining #database #developer #framework #platform
Data mining platform for database developers (AN), p. 14.
KDDKDD-2001-Ramakrishnan #collaboration #data mining
Mass collaboration and data mining (RR), p. 4.
KDDKDD-2001-TrainaTPF #data mining #multi #named #scalability #tool support
Tri-plots: scalable tools for multidimensional data mining (AJMT, CTJ, SP, CF), pp. 184–193.
KDDKDD-2001-WarnerRDB #interactive #knowledge base #web
Mining user session data to facilitate user interaction with a customer service knowledge base in RightNow Web (DW, JNR, SDD, BB), pp. 467–472.
KDDKDD-2001-YangWY #named
Infominer: mining surprising periodic patterns (JY, WW, PSY), pp. 395–400.
KDDKDD-2001-YangZL #modelling #predict #web
Mining web logs for prediction models in WWW caching and prefetching (QY, HHZ, ITYL), pp. 473–478.
KDDKDD-T-2001-FayyadRB #data mining #enterprise
E-business enterprise data mining (UMF, NR, PSB), pp. 1–85.
KDDKDD-T-2001-GallantPT #data mining #web
Value-based data mining and web mining for CRM (SG, GPS, MT), pp. 325–390.
KDDKDD-T-2001-HanLP #bibliography #perspective #scalability
Scalable frequent-pattern mining methods: an overview (JH, LVSL, JP), pp. 264–324.
KDDKDD-T-2001-KarguptaJ #data mining #distributed #mobile #ubiquitous
Data mining “to go”: ubiquitous KDD for mobile and distributed environments (HK, AJ), pp. 186–263.
KDDKDD-T-2001-Martin #data mining #robust #statistics
Data mining for outliers with robust statistics (RDM), pp. 86–118.
LSOLSO-2001-KrishnaswamyLZ #data mining #elicitation
Knowledge Elicitation through Web-Based Data Mining Services (SK, SWL, ABZ), pp. 120–134.
LSOLSO-2001-VossRMJ #collaboration #data mining #enterprise
Collaboration Support for Virtual Data Mining Enterprises (AV, GR, SM, AJ), pp. 83–95.
MLDMMLDM-2001-PernerB #data mining #hybrid
A Hybrid Tool for Data Mining in Picture Archiving System (PP, TPB), pp. 141–156.
MLDMMLDM-2001-SackK #data mining #evaluation
Evaluation of Clinical Relevance of Clinical Laboratory Investigations by Data Mining (US, MK), pp. 12–22.
MLDMMLDM-2001-SyG #analysis #approach #data mining #statistics
Data Mining Approach Based on Information-Statistical Analysis: Application to Temporal-Spatial Data (BKS, AKG), pp. 128–140.
MLDMMLDM-2001-Visa
Technology of Text Mining (AV), pp. 1–11.
SEKESEKE-2001-AlonsoCGM #data mining
Combining Expert Knowledge and Data Mining in a Medical Diagnosis Domain (FA, JPCV, ÁLG, CM), pp. 412–419.
SEKESEKE-2001-ConciMC #image
Image Mining by Color Coateat (AC, EMMMC), pp. 405–411.
SIGIRSIGIR-2001-LuLC #query
Anchor Text Mining for Translation Extraction of Query Terms (WHL, HJL, LFC), pp. 388–389.
TOOLSTOOLS-USA-2001-LiKLCL #algorithm #automation #data mining #parallel
Automatic Data Mining by Asynchronous Parallel Evolutionary Algorithms (JL, ZK, YL, HC, PL), pp. 99–107.
ICSEICSE-2001-Michail #data mining #library #named #reuse
CodeWeb: Data Mining Library Reuse Patterns (AM), pp. 827–828.
ICSEICSE-2001-SmithOB #analysis #architecture #component #product line #re-engineering
Mining Components for a Software Architecture and a Product Line: the Options Analysis for Reengineering (OAR) Method (DBS, LO, JB), p. 728.
HPDCHPDC-2001-KuntrarukP #data mining #distributed #feature model #parallel #using
Massively Parallel Distributed Feature Extraction in Textual Data Mining Using HDDI(tm) (JK, WMP), pp. 363–370.
SIGMODSIGMOD-2000-AgrawalS #data mining #privacy
Privacy-Preserving Data Mining (RA, RS), pp. 439–450.
SIGMODSIGMOD-2000-HanPY #generative
Mining Frequent Patterns without Candidate Generation (JH, JP, YY), pp. 1–12.
SIGMODSIGMOD-2000-HsuLG #image #information management
Image Mining in IRIS: Integrated Retinal Information System (WH, MLL, KGG), p. 593.
SIGMODSIGMOD-2000-PalmerF #clustering #data mining
Density Biased Sampling: An Improved Method for Data Mining and Clustering (CRP, CF), pp. 82–92.
SIGMODSIGMOD-2000-PeiMHZ #benchmark #data mining #metric #performance #towards
Towards Data Mining Benchmarking: A Testbed for Performance Study of Frequent Pattern Mining (JP, RM, KH, HZ), p. 592.
SIGMODSIGMOD-2000-RamaswamyRS #algorithm #performance #scalability #set
Efficient Algorithms for Mining Outliers from Large Data Sets (SR, RR, KS), pp. 427–438.
SIGMODSIGMOD-2000-RiedelFGN #data mining #for free
Data Mining on an OLTP System (Nearly) for Free (ER, CF, GRG, DN), pp. 13–21.
SIGMODSIGMOD-2000-ShenoyHSBBS #database #scalability
Turbo-charging Vertical Mining of Large Databases (PS, JRH, SS, GB, MB, DS), pp. 22–33.
SIGMODSIGMOD-2000-SzalayGKT #design #multi #overview
Designing and Mining Multi-Terabyte Astronomy Archives: The Sloan Digital Sky Survey (ASS, PZK, AT, JG, DRS, RJB), pp. 451–462.
VLDBVLDB-2000-ChakrabartiSST #collaboration #named
Memex: A Browsing Assistant for Collaborative Archiving and Mining of Surf Trails (SC, SS, MS, MT), pp. 603–606.
VLDBVLDB-2000-ChoenniV #algorithm #data mining #design #implementation
Design and Implementation of a Genetic-Based Algorithm for Data Mining (SC), pp. 33–42.
VLDBVLDB-2000-LakshmananJN #algebra #data mining
The 3W Model and Algebra for Unified Data Mining (TJ, LVSL, RTN), pp. 21–32.
VLDBVLDB-2000-NetzCBF #data mining #database #integration
Integration of Data Mining with Database Technology (AN, SC, JB, UMF), pp. 719–722.
VLDBVLDB-2000-Tsur #data mining
Data Mining in the Bioinformatics Domain (ST), pp. 711–714.
VLDBVLDB-2000-WangHH #constraints #using
Mining Frequent Itemsets Using Support Constraints (KW, YH, JH), pp. 43–52.
CSMRCSMR-2000-SartipiKM #architecture #data mining #design #using
Architectural Design Recovery using Data Mining Techniques (KS, KK, FM), pp. 129–140.
CIKMCIKM-2000-Zaki #category theory #constraints #sequence
Sequence Mining in Categorical Domains: Incorporating Constraints (MJZ), pp. 422–429.
ICMLICML-2000-MamitsukaA #database #learning #performance #query #scalability
Efficient Mining from Large Databases by Query Learning (HM, NA), pp. 575–582.
ICPRICPR-v1-2000-YouB #data mining #retrieval
Dynamic Shape Retrieval by Hierarchical Curve Matching, Snakes and Data Mining (JY, PB), pp. 5035–5038.
KDDKDD-2000-Bay #multi #set
Multivariate discretization of continuous variables for set mining (SDB), pp. 315–319.
KDDKDD-2000-BecherBF #automation #data analysis #data mining #performance
Automating exploratory data analysis for efficient data mining (JDB, PB, EF), pp. 424–429.
KDDKDD-2000-Bhattacharyya #algorithm #data mining #modelling #multi #performance
Evolutionary algorithms in data mining: multi-objective performance modeling for direct marketing (SB), pp. 465–473.
KDDKDD-2000-BrijsGSVW #data mining #framework
A data mining framework for optimal product selection in retail supermarket data: the generalized PROFSET model (TB, BG, GS, KV, GW), pp. 300–304.
KDDKDD-2000-Catlett #data mining #privacy
Among those dark electronic mills: privacy and data mining (JC), p. 4.
KDDKDD-2000-DhondGV #data mining #optimisation
Data mining techniques for optimizing inventories for electronic commerce (AD, AG, SV), pp. 480–486.
KDDKDD-2000-DomingosH #data type #performance
Mining high-speed data streams (PMD, GH), pp. 71–80.
KDDKDD-2000-FountainDS #testing
Mining IC test data to optimize VLSI testing (TF, TGD, BS), pp. 18–25.
KDDKDD-2000-GardnerB #data mining #problem
Data mining solves tough semiconductor manufacturing problems (MG, JB), pp. 376–383.
KDDKDD-2000-GavrilovAIM #question
Mining the stock market (extended abstract): which measure is best? (MG, DA, PI, RM), pp. 487–496.
KDDKDD-2000-HanPMCDH #named
FreeSpan: frequent pattern-projected sequential pattern mining (JH, JP, BMA, QC, UD, MH), pp. 355–359.
KDDKDD-2000-HsuLLL #database
Exploration mining in diabetic patients databases: findings and conclusions (WH, MLL, BL, TWL), pp. 430–436.
KDDKDD-2000-KingKCD #data mining #functional #predict #sequence #using
Genome scale prediction of protein functional class from sequence using data mining (RDK, AK, AC, LD), pp. 384–389.
KDDKDD-2000-MaLWYL #data mining #student #using
Targeting the right students using data mining (YM, BL, CKW, PSY, SML), pp. 457–464.
KDDKDD-2000-Papadimitriou #data mining #on the
On certain rigorous approaches to data mining (CHP), p. 2.
KDDKDD-2000-PeiH #constraints #question
Can we push more constraints into frequent pattern mining? (JP, JH), pp. 350–354.
KDDKDD-2000-PenaFL #behaviour #data mining #detection
Data mining to detect abnormal behavior in aerospace data (JMP, FF, SL), pp. 390–397.
KDDKDD-2000-Stodder #data mining
After the gold rush (invited talk, abstract only): data mining in the new economy (DS), p. 7.
KDDKDD-2000-TanBHG #data mining
Textual data mining of service center call records (PNT, HB, SAH, RPG), pp. 417–423.
KDDKDD-2000-WangMSW #biology #case study #classification #data mining #network #sequence
Application of neural networks to biological data mining: a case study in protein sequence classification (JTLW, QM, DS, CHW), pp. 305–309.
KDDKDD-2000-WangYY #performance
Efficient mining of weighted association rules (WAR) (WW, JY, PSY), pp. 270–274.
KDDKDD-2000-YangWY
Mining asynchronous periodic patterns in time series data (JY, WW, PSY), pp. 275–279.
PADLPADL-2000-HuCT #algorithm #analysis #data mining
Calculating a New Data Mining Algorithm for Market Basket Analysis (ZH, WNC, MT), pp. 169–184.
ICSEICSE-2000-Michail #data mining #library #reuse #using
Data mining library reuse patterns using generalized association rules (AM), pp. 167–176.
SACSAC-2000-DongPDZ
The Application of Association Rule Mining to Remotely Sensed Data (JD, WP, QD, JZ), pp. 340–345.
SACSAC-2000-GoodwinM #data mining #predict
Data Mining for Preterm Birth Prediction (LKG, SM), pp. 46–51.
SACSAC-2000-HongKCW #algorithm #data-driven #database #fuzzy
Mining Fuzzy Rules from Quantitative Data Based on the AprioriTid Algorithm (TPH, CSK, SCC, SLW), pp. 534–536.
SACSAC-2000-PsailaL
Hierarchy-based Mining of Association Rules in Data Warehouses (GP, PLL), pp. 307–312.
SACSAC-2000-WangTH #database #dependence #functional #fuzzy #relational
Mining Functional Dependencies from Fuzzy Relational Databases (SLW, JST, TPH), pp. 490–493.
HPDCHPDC-2000-HinkeN #data mining #grid #power management
Data Mining on NASA’s Information Power Grid (THH, JN), pp. 292–293.
ICLPCL-2000-BastidePTSL #using
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets (YB, NP, RT, GS, LL), pp. 972–986.
SIGMODSIGMOD-1999-Chakrabarti #data mining #database #hypermedia
Hypertext Databases and Data Mining (SC), p. 508.
SIGMODSIGMOD-1999-Hidber #online
Online Association Rule Mining (CH), pp. 145–156.
SIGMODSIGMOD-1999-NgLHM #query #set
Exploratory Mining via Constrained Frequent Set Queries (RTN, LVSL, JH, TM), pp. 556–558.
VLDBVLDB-1999-GarofalakisRS #constraints #named #regular expression
SPIRIT: Sequential Pattern Mining with Regular Expression Constraints (MNG, RR, KS), pp. 223–234.
VLDBVLDB-1999-KoudasMJ #database
Mining Deviants in a Time Series Database (HVJ, NK, SM), pp. 102–113.
VLDBVLDB-1999-TamuraK #clustering #parallel
Dynamic Load Balancing for Parallel Association Rule Mining on Heterogenous PC Cluster Systems (MT, MK), pp. 162–173.
ICEISICEIS-1999-DuKM #algorithm #performance #reuse
Two Fast Algorithms for Repeated Mining of Association Rules Based on Ressource Reuse (XD, KK, AM), pp. 65–73.
ICEISICEIS-1999-RamanA #algorithm
Candidate Drop Algorithm for Mining Association Rules (SR, TSA), pp. 132–139.
CIKMCIKM-1999-FengLYH #transaction
Mining Inter-Transaction Associations with Templates (LF, HL, JXY, JH), pp. 225–233.
CIKMCIKM-1999-HoltC #database #performance
Efficient Mining of Association Rules in Text Databases (JDH, SMC), pp. 234–242.
CIKMCIKM-1999-ParthasarathyZOD #incremental #interactive #sequence
Incremental and Interactive Sequence Mining (SP, MJZ, MO, SD), pp. 251–258.
KDDKDD-1999-Agrawal #data mining
Data Mining: Crossing the Chasm (RA), p. 2.
KDDKDD-1999-BayardoA
Mining the Most Interesting Rules (RJBJ, RA), pp. 145–154.
KDDKDD-1999-BayP #category theory #data mining #detection #set
Detecting Change in Categorical Data: Mining Contrast Sets (SDB, MJP), pp. 302–306.
KDDKDD-1999-BrinRS
Mining Optimized Gain Rules for Numeric Attributes (SB, RR, KS), pp. 135–144.
KDDKDD-1999-BuntineFP #automation #data mining #source code #synthesis #towards
Towards Automated Synthesis of Data Mining Programs (WLB, BF, TP), pp. 372–376.
KDDKDD-1999-ChengFZ #clustering
Entropy-based Subspace Clustering for Mining Numerical Data (CHC, AWCF, YZ), pp. 84–93.
KDDKDD-1999-CortesP #agile #deployment #framework #platform
Information Mining Platforms: An Infrastructure for KDD Rapid Deployment (CC, DP), pp. 327–331.
KDDKDD-1999-DongL #difference #performance #roadmap
Efficient Mining of Emerging Patterns: Discovering Trends and Differences (GD, JL), pp. 43–52.
KDDKDD-1999-DorreGS
Text Mining: Finding Nuggets in Mountains of Textual Data (JD, PG, RS), pp. 398–401.
KDDKDD-1999-HotzNPS #data mining #industrial
WAPS, a Data Mining Support Environment for the Planning of Warranty and Goodwill Costs in the Automobile Industry (EH, GN, BP, HS), pp. 417–419.
KDDKDD-1999-LarsenA #clustering #documentation #effectiveness #linear #performance #using
Fast and Effective Text Mining Using Linear-Time Document Clustering (BL, CA), pp. 16–22.
KDDKDD-1999-LeeSM #data flow #detection #experience #network
Mining in a Data-Flow Environment: Experience in Network Intrusion Detection (WL, SJS, KWM), pp. 114–124.
KDDKDD-1999-LeshZO #classification #sequence
Mining Features for Sequence Classification (NL, MJZ, MO), pp. 342–346.
KDDKDD-1999-LiuHM99a #multi
Mining Association Rules with Multiple Minimum Supports (BL, WH, YM), pp. 337–341.
KDDKDD-1999-LiuHMC #using
Mining Interesting Knowledge Using DM-II (BL, WH, YM, SC), pp. 430–434.
KDDKDD-1999-ManiDBD #data mining #modelling #statistics
Statistics and Data Mining Techniques for Lifetime Value Modeling (DRM, JD, AB, PD), pp. 94–103.
KDDKDD-1999-MegalooikonomouDH #database #image
Mining Lesion-Deficit Associations in a Brain Image Database (VM, CD, EH), pp. 347–351.
KDDKDD-1999-RogersLW #modelling
Mining GPS Data to Augment Road Models (SR, PL, CW), pp. 104–113.
KDDKDD-1999-TungLHF #transaction
Breaking the Barrier of Transactions: Mining Inter-Transaction Association Rules (AKHT, HL, JH, LF), pp. 297–301.
KDDKDD-1999-WangWLSSZ #algorithm #clustering #data mining
Evaluating a Class of Distance-Mapping Algorithms for Data Mining and Clustering (JTLW, XW, KIL, DS, BAS, KZ), pp. 307–311.
KDDKDD-T-1999-Feldman #semistructured data
Mining Unstructured Data (RF), pp. 182–236.
KDDKDD-T-1999-Holsheimer #data mining #process
Data Mining by Business Users: Integrating Data Mining in Business Processes (MH), pp. 266–291.
KDDKDD-T-1999-RastogiS #algorithm #database #scalability
Scalable Algorithms for Mining Large Databases (RR, KS), pp. 73–140.
MLDMMLDM-1999-HongW #data mining #predict
Advanced in Predictive Data Mining Methods (SJH, SMW), pp. 13–20.
MLDMMLDM-1999-Sawaragi #data mining #effectiveness #interactive
Reproductive Process-Oriented Data Mining from Interactions between Human and Complex Artifact System (TS), pp. 180–194.
MLDMMLDM-1999-Scaringella #data mining #monitoring #risk management
A Data Mining Application for Monitoring Environmental Risks (AS), pp. 209–215.
SIGIRSIGIR-1999-NieSID #automation #information retrieval #parallel #web
Cross-Language Information Retrieval Based on Parallel Texts and Automatic Mining of Parallel Texts from the Web (JYN, MS, PI, RD), pp. 74–81.
ASEASE-1999-Michail #data mining #library #reuse
Data Mining Library Reuse Patterns in User-Selected Applications (AM), p. 24–?.
ADLADL-1998-AhonenHKV #data mining #documentation
Applying Data Mining Techniques for Descriptive Phrase Extraction in Digital Document Collections (HA, OH, MK, AIV), pp. 2–11.
ADLADL-1998-FengF #algorithm
Binary Partition Based Algorithms for Mining Association Rules (JF, YF), pp. 30–34.
ADLADL-1998-ZaianeXH #data access #data mining #roadmap #web
Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs (ORZ, MX, JH), pp. 19–29.
SIGMODSIGMOD-1998-AgrawalGGR #automation #clustering #data mining
Automatic Subspace Clustering of High Dimensional Data for Data Mining Applications (RA, JG, DG, PR), pp. 94–105.
SIGMODSIGMOD-1998-Bayardo #database
Efficiently Mining Long Patterns from Databases (RJBJ), pp. 85–93.
SIGMODSIGMOD-1998-LiS #data mining #parallel
Free Parallel Data Mining (BL, DS), pp. 541–543.
SIGMODSIGMOD-1998-NgLHP #optimisation
Exploratory Mining and Pruning Optimizations of Constrained Association Rules (RTN, LVSL, JH, AP), pp. 13–24.
SIGMODSIGMOD-1998-NgLK #data mining
A Data Mining Application: Customes Retention at the Port of Singapore Authority (PSA) (KN, HL, HK), pp. 522–525.
SIGMODSIGMOD-1998-SarawagiTA #database #relational
Integrating Mining with Relational Database Systems: Alternatives and Implications (SS, ST, RA), pp. 343–354.
SIGMODSIGMOD-1998-ShintaniK #algorithm #classification #parallel
Parallel Mining Algorithms for Generalized Association Rules with Classification Hierarchy (TS, MK), pp. 25–36.
SIGMODSIGMOD-1998-TsurUACMNR #query
Query Flocks: A Generalization of Association-Rule Mining (ST, JDU, SA, CC, RM, SN, AR), pp. 1–12.
SIGMODSIGMOD-1998-ZaianeHLCC #data mining #multi #named #prototype
MultiMediaMiner: A System Prototype for Multimedia Data Mining (ORZ, JH, ZNL, SHSC, JC), pp. 581–583.
VLDBVLDB-1998-ChakrabartiSD #using
Mining Surprising Patterns Using Temporal Description Length (SC, SS, BD), pp. 606–617.
VLDBVLDB-1998-EsterKSWX #clustering #incremental
Incremental Clustering for Mining in a Data Warehousing Environment (ME, HPK, JS, MW, XX), pp. 323–333.
VLDBVLDB-1998-George #data mining #named #parallel
DMS: A Parallel Data Mining Server (FAWG), p. 702.
VLDBVLDB-1998-KnorrN #algorithm #dataset #scalability
Algorithms for Mining Distance-Based Outliers in Large Datasets (EMK, RTN), pp. 392–403.
VLDBVLDB-1998-KornLKF #data mining #paradigm #performance
Ratio Rules: A New Paradigm for Fast, Quantifiable Data Mining (FK, AL, YK, CF), pp. 582–593.
VLDBVLDB-1998-MorimotoFMTY #algorithm #category theory #database
Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases (YM, TF, HM, TT, KY), pp. 380–391.
VLDBVLDB-1998-RiedelGF #data mining #multi #scalability
Active Storage for Large-Scale Data Mining and Multimedia (ER, GAG, CF), pp. 62–73.
VLDBVLDB-1998-SilversteinBMU #scalability
Scalable Techniques for Mining Causal Structures (CS, SB, RM, JDU), pp. 594–605.
ICSMEICSM-1998-OcaC #data mining #identification #using
Identification of Data Cohesive Subsystems Using Data Mining Techniques (CMdO, DLC), pp. 16–23.
WCREWCRE-1998-Dayani-FardJ #repository #reverse engineering
Reverse Engineering by Mining Dynamic Repositories (HDF, IJ), pp. 174–182.
CIKMCIKM-1998-HaradaAOT #parallel
Dynamic Skew Handling in Parallel Mining of Association Rules (LH, NA, KO, RT), pp. 76–85.
CIKMCIKM-1998-LiYC #abstraction #database #framework #multi #named #sequence
MALM: A Framework for Mining Sequence Database at Multiple Abstraction Levels (CSL, PSY, VC), pp. 267–272.
CIKMCIKM-1998-TungTL #named #online #optimisation #using
BROOM: Buffer Replacement using Online Optimization by Mining (AKHT, YCT, HL), pp. 185–192.
KDDKDD-1998-BerchtoldJR #data mining #diagrams #independence #visual notation
Independence Diagrams: A Technique for Visual Data Mining (SB, HVJ, KAR), pp. 139–143.
KDDKDD-1998-BorgesL #database #hypermedia
Mining Association Rules in Hypertext Databases (JB, ML), pp. 149–153.
KDDKDD-1998-FeeldersCM #bias
Mining in the Presence of Selectivity Bias and its Application to Reject Inference (AJF, SC, GJM), pp. 199–203.
KDDKDD-1998-GrecuB #data mining #distributed #learning
Coactive Learning for Distributed Data Mining (DLG, LAB), pp. 209–213.
KDDKDD-1998-GuralnikWS #sequence
Pattern Directed Mining of Sequence Data (VG, DW, JS), pp. 51–57.
KDDKDD-1998-HanGY #database
Mining Segment-Wise Periodic Patterns in Time-Related Databases (JH, WG, YY), pp. 214–218.
KDDKDD-1998-KellyHA #data mining #performance
Defining the Goals to Optimise Data Mining Performance (MGK, DJH, NMA), pp. 234–238.
KDDKDD-1998-LeeSM #detection #modelling
Mining Audit Data to Build Intrusion Detection Models (WL, SJS, KWM), pp. 66–72.
KDDKDD-1998-LingL #data mining #problem
Data Mining for Direct Marketing: Problems and Solutions (CXL, CL), pp. 73–79.
KDDKDD-1998-LiuHM #classification
Integrating Classification and Association Rule Mining (BL, WH, YM), pp. 80–86.
KDDKDD-1998-NakhaeizadehS #algorithm #data mining #evaluation #personalisation #towards
Towards the Personalization of Algorithms Evaluation in Data Mining (GN, AS), pp. 289–293.
KDDKDD-1998-ParthasarathyZL #memory management #parallel
Memory Placement Techniques for Parallel Association Mining (SP, MJZ, WL), pp. 304–308.
KDDKDD-1998-PinheiroS #database #semistructured data
Methods for Linking and Mining Massive Heterogeneous Databases (JCP, DXS), pp. 309–313.
KDDKDD-1998-SinghCHSA #architecture #robust
A Robust System Architecture for Mining Semi-Structured Data (LS, BC, RH, PS, KA), pp. 329–333.
KDDKDD-1998-StaudtKR #data mining
A Data Mining Support Environment and its Application on Insurance Data (MS, JUK, UR), pp. 105–111.
KDDKDD-1998-Stolfo #classification #database
Mining Databases with Different Schemas: Integrating Incompatible Classifiers (ALP, SJS), pp. 314–318.
KDDKDD-1998-Subramonian #data mining
Defining diff as a Data Mining Primitive (RS), pp. 334–338.
KDDKDD-1998-ThomasS #query #sql #using
Mining Generalized Association Rules and Sequential Patterns Using SQL Queries (ST, SS), pp. 344–348.
KDDKDD-1998-ZakiLO #named #sequence
PlanMine: Sequence Mining for Plan Failures (MJZ, NL, MO), pp. 369–374.
SIGIRSIGIR-1998-LinSCHKH #approach #classification #documentation #internet #semantics
Extracting Classification Knowledge of Internet Documents with Mining Term Associations: A Semantic Approach (SHL, CSS, MCC, JMH, MTK, YMH), pp. 241–249.
TOOLSTOOLS-ASIA-1998-WangH #data mining #implementation #research
The Research and Implementation of Data Warehouse and Data Mining in Decision Support System (BW, JH), pp. 362–371.
SACSAC-1998-MachucaM #data mining #database #precise #relational #set
Enhancing the exploitation of data mining in relational database systems via the rough sets theory including precision variables (FM, MM), pp. 70–73.
HPDCHPDC-1998-OguchiSTK #clustering #data mining #effectiveness #evaluation #optimisation #parallel #parametricity #protocol #scalability
Optimizing Protocol Parameters to Large Scale PC Cluster and Evaluation of its Effectiveness with Parallel Data Mining (MO, TS, TT, MK), pp. 34–41.
PODSPODS-1997-GunopulosKMT #data mining #machine learning
Data mining, Hypergraph Transversals, and Machine Learning (DG, RK, HM, HT), pp. 209–216.
SIGMODSIGMOD-1997-HanKK #data mining #parallel #scalability
Scalable Parallel Data Mining for Association Rules (EHH, GK, VK), pp. 277–288.
SIGMODSIGMOD-1997-HanKS #data mining #named #prototype
GeoMiner: A System Prototype for Spatial Data Mining (JH, KK, NS), pp. 553–556.
VLDBVLDB-1997-KietzRS
Mining Insurance Data at Swiss Life (JUK, UR, MS), pp. 562–566.
VLDBVLDB-1997-ShaferA #algorithm #data mining #parallel #similarity
Parallel Algorithms for High-dimensional Similarity Joins for Data Mining Applications (JCS, RA), pp. 176–185.
VLDBVLDB-1997-WangYM #approach #data mining #grid #named #statistics
STING: A Statistical Information Grid Approach to Spatial Data Mining (WW, JY, RRM), pp. 186–195.
CIKMCIKM-1997-ChanA #fuzzy
Mining Fuzzy Association Rules (KCCC, WHA), pp. 209–215.
CIKMCIKM-1997-HahnS
Knowledge Mining from Textual Sources (UH, KS), pp. 83–90.
CIKMCIKM-1997-ParkYC
Mining Association Rules with Adjustable Accuracy (JSP, PSY, MSC), pp. 151–160.
CIKMCIKM-1997-YoonSP #data mining #query #using
Intensional Query Processing Using Data Mining Approaches (SCY, IYS, EKP), pp. 201–208.
KDDKDD-1997-AronisP #algorithm #data mining #performance
Increasing the Efficiency of Data Mining Algorithms with Breadth-First Marker Propagation (JMA, FJP), pp. 119–122.
KDDKDD-1997-Bayardo #classification
Brute-Force Mining of High-Confidence Classification Rules (RJBJ), pp. 123–126.
KDDKDD-1997-BergstenSS #analysis #data mining #machine learning
Applying Data Mining and Machine Learning Techniques to Submarine Intelligence Analysis (UB, JS, PS), pp. 127–130.
KDDKDD-1997-BrunkKK #data mining #named
MineSet: An Integrated System for Data Mining (CB, JK, RK), pp. 135–138.
KDDKDD-1997-ChattratichatDGGHKSTY #challenge #data mining #scalability
Large Scale Data Mining: Challenges and Responses (JC, JD, MG, YG, HH, MK, JS, HWT, DY), pp. 143–146.
KDDKDD-1997-DeCoste #behaviour #multi
Mining Multivariate Time-Series Sensor Data to Discover Behavior Envelopes (DD), pp. 151–154.
KDDKDD-1997-EngelsLS #data mining
A Guided Tour through the Data Mining Jungle (RE, GL, RS), pp. 163–166.
KDDKDD-1997-FeldmanAAZK #documentation #keyword
Maximal Association Rules: A New Tool for Mining for Keyword Co-Occurrences in Document Collections (RF, YA, AA, AZ, WK), pp. 167–170.
KDDKDD-1997-FeldmanKZ #data mining #documentation #visualisation
Visualization Techniques to Explore Data Mining Results for Document Collections (RF, WK, AZ), pp. 16–23.
KDDKDD-1997-HaimowitzGS #database #distributed
Integrating and Mining Distributed Customer Databases (IJH, ÖGA, HS), pp. 179–182.
KDDKDD-1997-HinkeRRG #independence #roadmap
Target-Independent Mining for Scientific Data: Capturing Transients and Trends for Phenomena Mining (THH, JAR, HSR, SJG), pp. 187–190.
KDDKDD-1997-KahngM #algorithm
Mining Generalized Term Associations: Count Propagation Algorithm (JK, WHKL, DM), pp. 203–206.
KDDKDD-1997-KamberHC #multi #using
Metarule-Guided Mining of Multi-Dimensional Association Rules Using Data Cubes (MK, JH, JC), pp. 207–210.
KDDKDD-1997-KarguptaHS #architecture #data mining #distributed #scalability
Scalable, Distributed Data Mining — An Agent Architecture (HK, IH, BS), pp. 211–214.
KDDKDD-1997-KramerPH #machine learning
Mining for Causes of Cancer: Machine Learning Experiments at Various Levels of Detail (SK, BP, CH), pp. 223–226.
KDDKDD-1997-MihalisinT #data mining #performance #robust #visual notation
Fast Robust Visual Data Mining (TM, JT), pp. 231–234.
KDDKDD-1997-NakhaeizadehS #algorithm #data mining #development #evaluation #metric #multi
Development of Multi-Criteria Metrics for Evaluation of Data Mining Algorithms (GN, AS), pp. 37–42.
KDDKDD-1997-SrikantVA #constraints
Mining Association Rules with Item Constraints (RS, QV, RA), pp. 67–73.
KDDKDD-1997-ZupanBBC #approach #composition #data mining #dataset
A Dataset Decomposition Approach to Data Mining and Machine Discovery (BZ, MB, IB, BC), pp. 299–302.
TOOLSTOOLS-ASIA-1997-Dai #approach #data mining #database #integration #multi #object-oriented
An Object-Oriented Approach to Schema Integration and Data Mining in Multiple Databases (HD), pp. 294–303.
SACSAC-1997-ChanA #algorithm #effectiveness
An effective algorithm for mining interesting quantitative association rules (KCCC, WHA), pp. 88–90.
SACSAC-1997-GoliP #data mining #multi
Application of domain vector perfect hash join for multimedia data mining (VNRG, WP), pp. 334–339.
SACSAC-1997-Mazlack #database
Developing a focus in unsupervised database mining (LJM), pp. 187–191.
ICLPILPS-1997-Mannila #data mining #database #induction
Inductive Databases and Condensed Representations for Data Mining (HM), pp. 21–30.
PODSPODS-1996-BettiniWJ #data mining #multi #testing
Testing Complex Temporal Relationships Involving Multiple Granularities and Its Application to Data Mining (CB, XSW, SJ), pp. 68–78.
PODSPODS-1996-FukudaMMT
Mining Optimized Association Rules for Numeric Attributes (TF, YM, SM, TT), pp. 182–191.
SIGMODSIGMOD-1996-FukudaMMT #2d #algorithm #data mining #using #visualisation
Data Mining Using Two-Dimensional Optimized Accociation Rules: Scheme, Algorithms, and Visualization (TF, YM, SM, TT), pp. 13–23.
SIGMODSIGMOD-1996-Han #data mining
Data Mining Techniques (JH), p. 545.
SIGMODSIGMOD-1996-HanFWCZK #database #interactive #multi #named #relational
DBMiner: Interactive Mining of Multiple-Level Knowledge in Relational Databases (JH, YF, WW, JC, ORZ, KK), p. 550.
SIGMODSIGMOD-1996-SrikantA #relational #scalability
Mining Quantitative Association Rules in Large Relational Tables (RS, RA), pp. 1–12.
VLDBVLDB-1996-MeoPC
A New SQL-like Operator for Mining Association Rules (RM, GP, SC), pp. 122–133.
VLDBVLDB-1996-MiningGroup #data mining #visualisation
MineSet(tm): A System for High-End Data Mining and Visualization, p. 595.
VLDBVLDB-1996-ShaferAM #classification #data mining #named #parallel #scalability
SPRINT: A Scalable Parallel Classifier for Data Mining (JCS, RA, MM), pp. 544–555.
VLDBVLDB-1996-ViverosNR #data mining #health #information management
Applying Data Mining Techniques to a Health Insurance Information System (MSV, JPN, MJR), pp. 286–294.
KDDAKDDM-1996-FayyadPS #data mining #information management #overview #perspective
From Data Mining to Knowledge Discovery: An Overview (UMF, GPS, PS), pp. 1–34.
KDDAKDDM-1996-HanF #data mining #induction
Attribute-Oriented Induction in data Mining (JH, YF), pp. 399–421.
KDDAKDDM-1996-Piatetsky-Shapiro #data mining #information management #internet
Data Mining and Knowledge Discovery Internet Resources (GPS), pp. 593–595.
KDDAKDDM-1996-ShenOMZ #data mining
Metaqueries for Data Mining (WMS, KO, BGM, CZ), pp. 375–398.
KDDAKDDM-1996-SimoudisLK #data mining #deduction #induction #reasoning
Integrating Inductive and Deductive Reasoning for Data Mining (ES, BL, RK), pp. 353–373.
KDDAKDDM-1996-Uthurusamy #challenge #data mining #information management
From Data Mining to Knowledge Discovery: Current Challenges and Future Directions (RU), pp. 561–569.
CIKMCIKM-1996-FortinL #approach #multi #object-oriented
An Object-Oriented Approach to Multi-Level Association Rule Mining (SF, LL), pp. 65–72.
CIKMCIKM-1996-HanP #algorithm
Background for Association Rules and Cost Estimate of Selected Mining Algorithms (JLH, AWP), pp. 73–80.
ICMLICML-1996-Mannila #data mining #machine learning
Data Mining and Machine Learning (HM), p. 555.
KDDKDD-1996-AgrawalMSSAB #data mining
The Quest Data Mining System (RA, MM, JCS, RS, AA, TB), pp. 244–249.
KDDKDD-1996-AgrawalS #data mining #database #relational
Developing Tightly-Coupled Data Mining Applications on a Relational Database System (RA, KS), pp. 287–290.
KDDKDD-1996-CiesielskiP #database #hybrid #overview #using
Using a Hybrid Neural/Expert System for Data Base Mining in Market Survey Data (VC, GP), pp. 38–43.
KDDKDD-1996-Czyzewski
Mining Knowledge in Noisy Audio Data (AC), pp. 220–225.
KDDKDD-1996-Fahner #data mining #interactive
Data Mining with Sparse and Simplified Interaction Selection (GF), pp. 359–362.
KDDKDD-1996-FawcettP #data mining #effectiveness #machine learning #profiling
Combining Data Mining and Machine Learning for Effective User Profiling (TF, FJP), pp. 8–13.
KDDKDD-1996-FayyadPS #data mining #framework #information management #towards
Knowledge Discovery and Data Mining: Towards a Unifying Framework (UMF, GPS, PS), pp. 82–88.
KDDKDD-1996-FeldmanH
Mining Associations in Text in the Presence of Background Knowledge (RF, HH), pp. 343–346.
KDDKDD-1996-FlockhartR #approach #data mining #search-based
A Genetic Algorithm-Based Approach to Data Mining (IWF, NJR), pp. 299–302.
KDDKDD-1996-FultonKSW #data mining #induction #interactive #towards
Local Induction of Decision Trees: Towards Interactive Data Mining (TF, SK, SS, DLW), pp. 14–19.
KDDKDD-1996-GaneshSR #database #identification #integration
Mining Entity-Identification Rules for Database Integration (MG, JS, TR), pp. 291–294.
KDDKDD-1996-GrossmanBNP #data mining #optimisation
Data Mining and Tree-Based Optimization (RLG, HB, DN, HVP), pp. 323–326.
KDDKDD-1996-HanFWCGKLLRSXZ #database #named #relational #scalability
DBMiner: A System for Mining Knowledge in Large Relational Databases (JH, YF, WW, JC, WG, KK, DL, YL, AR, NS, BX, ORZ), pp. 250–255.
KDDKDD-1996-ImielinskiVA #database #interface #named #programming #query
DataMine: Application Programming Interface and Query Language for Database Mining (TI, AV, AA), pp. 256–262.
KDDKDD-1996-JohnL #data mining
Static Versus Dynamic Sampling for Data Mining (GHJ, PL), pp. 367–370.
KDDKDD-1996-KontkanenMT #data mining #finite #predict
Predictive Data Mining with Finite Mixtures (PK, PM, HT), pp. 176–182.
KDDKDD-1996-Piatetsky-ShapiroBKKS #data mining #industrial #information management #overview
An Overview of Issues in Developing Industrial Data Mining and Knowledge Discovery Applications (GPS, RJB, TK, WK, ES), pp. 89–95.
KDDKDD-1996-ProvanS #case study #data mining
Data Mining and Model Simplicity: A Case Study in Diagnosis (GMP, MS), pp. 57–62.
KDDKDD-1996-ShekMMN #data mining #distributed #scalability
Scalable Exploratory Data Mining of Distributed Geoscientific Data (ECS, RRM, EM, KWN), pp. 32–37.
KDDKDD-1996-ShenL #data mining #generative
Metapattern Generation for Integrated Data Mining (WMS, BL), pp. 152–157.
KDDKDD-1996-StolorzD #data mining #detection #named #scalability
Quakefinder: A Scalable Data Mining System for Detecting Earthquakes from Space (PES, CD), pp. 208–213.
KDDKDD-1996-WrobelWSE #data mining
Extensibility in Data Mining Systems (SW, DW, ES, WE), pp. 214–219.
KDDKDD-1996-ZytkowZ #automation
Automated Pattern Mining with a Scale Dimension (JMZ, RZ), pp. 158–163.
SEKESEKE-1996-ChangW #case study #data mining
Scientific Data Mining: A Case Study (CYC, JTLW), pp. 100–107.
SIGMODSIGMOD-1995-ParkCY #algorithm #effectiveness
An Effective Hash Based Algorithm for Mining Association Rules (JSP, MSC, PSY), pp. 175–186.
VLDBVLDB-1995-LuSL #approach #data mining #named
NeuroRule: A Connectionist Approach to Data Mining (HL, RS, HL), pp. 478–489.
VLDBVLDB-1995-SavasereON #algorithm #database #performance #scalability
An Efficient Algorithm for Mining Association Rules in Large Databases (AS, EO, SBN), pp. 432–444.
VLDBVLDB-1995-SrikantA
Mining Generalized Association Rules (RS, RA), pp. 407–419.
CIKMCIKM-1995-AnandBH #data mining
The Role of Domain Knowledge in Data Mining (SSA, DAB, JGH), pp. 37–43.
CIKMCIKM-1995-Han #concept #multi
Mining Knowledge at Multiple Concept Levels (JH), pp. 19–24.
CIKMCIKM-1995-ParkCY #data mining #parallel #performance
Efficient Parallel and Data Mining for Association Rules (JSP, MSC, PSY), pp. 31–36.
KDDKDD-1995-AgrawalP #data mining
Active Data Mining (RA, GP), pp. 3–8.
KDDKDD-1995-DaoP #integration
Applying a Data Miner To Heterogeneous Schema Integration (SD, BP), pp. 63–68.
KDDKDD-1995-FeeldersLZ #case study #data mining #evaluation
Data Mining for Loan Evaluation at ABN AMRO: A Case Study (AJF, AJFlL, JWvZ), pp. 106–111.
KDDKDD-1995-HolsheimerKMT #data mining #database
A Perspective on Databases and Data Mining (MH, MLK, HM, HT), pp. 150–155.
KDDKDD-1995-SeshadriSW #data mining #feature model
Feature Extraction for Massive Data Mining (VS, RS, SMW), pp. 258–262.
KDDKDD-1995-StolorzNMMSSYNCMF #data mining #dataset #performance #scalability
Fast Spatio-Temporal Data Mining of Large Geophysical Datasets (PES, HN, EM, RRM, ECS, JRS, JY, KWN, SYC, CRM, JDF), pp. 300–305.
PODSPODS-1994-Agrawal #database #tutorial
Tutorial Database Mining (RA), pp. 75–76.
SIGMODSIGMOD-1994-AgrawalCFGHIIMMSS #database #named
Quest: A Project on Database Mining (RA, MJC, CF, SPG, MAWH, TI, BRI, AM, HM, RS, ANS), p. 514.
VLDBVLDB-1994-AgrawalS #algorithm #database #performance #scalability
Fast Algorithms for Mining Association Rules in Large Databases (RA, RS), pp. 487–499.
VLDBVLDB-1994-NgH #clustering #data mining #effectiveness #performance
Efficient and Effective Clustering Methods for Spatial Data Mining (RTN, JH), pp. 144–155.
KDDKDD-1994-AnandBH #architecture #database #optimisation #preprocessor #query #semantics
Database Mining in the Architecture of a Semantic Preprocessor for State Aware Query Optimization (SSA, DAB, JGH), pp. 287–298.
KDDKDD-1994-HolsheimerK #architecture #data mining
Architectural Support for Data Mining (MH, MLK), pp. 217–228.
KDDKDD-1994-SimoudisLK #database #deduction #induction #reasoning
Integrating Inductive and Deductive Reasoning for Database Mining (ES, BL, RK), pp. 37–48.
SIGMODSIGMOD-1993-AgrawalIS #database #scalability #set
Mining Association Rules between Sets of Items in Large Databases (RA, TI, ANS), pp. 207–216.
HCIHCI-ACS-1993-KeranRS #automation #mobile #perspective
Automation of Mobile Equipment in Mining: A Human Factors Perspective (CMK, RFR, TJS), pp. 307–312.
HCIHCI-ACS-1993-NtuenPK #architecture #human-computer #interface
A Blackboard Architecture for Human-Machine Interface in a Mining Teleoperation (CAN, EHP, SMK), pp. 50–55.
CIKMCIKM-1993-CrompC #data mining #image #multi
Data Mining of Multi-dimensional Remotely Sensed Images (RFC, WJC), pp. 471–480.
ICMLICML-1993-RaoVF #data mining
Data Mining of Subjective Agricultural Data (RBR, TBV, TWF), pp. 244–251.
VLDBVLDB-1992-AgrawalGIIS #classification #database
An Interval Classifier for Database Mining Applications (RA, SPG, TI, BRI, ANS), pp. 560–573.
KDDKDD-1991-KaufmanMK #database
Mining for Knowledge in Databases: Goals and General Description of the INLEN System (KAK, RSM, LK), pp. 449–464.
KDDKDD-1991-ZytkowB #database #interactive
Interactive Mining of Regularities in Databases (JMZ, JB), pp. 31–54.

Bibliography of Software Language Engineering in Generated Hypertext (BibSLEIGH) is created and maintained by Dr. Vadim Zaytsev.
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