Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining
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Bharat Rao, Balaji Krishnapuram, Andrew Tomkins, Qiang Yang
Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining
KDD, 2010.

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@proceedings{KDD-2010,
	address       = "Washington, District of Columbia, USA",
	editor        = "Bharat Rao and Balaji Krishnapuram and Andrew Tomkins and Qiang Yang",
	isbn          = "978-1-4503-0055-1",
	publisher     = "{ACM}",
	title         = "{Proceedings of the 16th International Conference on Knowledge Discovery and Data Mining}",
	year          = 2010,
}

Contents (125 items)

KDD-2010-Lu #data mining #industrial #mining #online
Data mining in the online services industry (QL), pp. 1–2.
KDD-2010-Freund
Data winnowing (YF), pp. 3–4.
KDD-2010-Feldman #data mining #lessons learnt #mining #quantifier #scalability
The quantification of advertising: (+ lessons from building businesses based on large scale data mining) (KF), pp. 5–6.
KDD-2010-ChanGGHL #modelling #online #pipes and filters #scalability
Evaluating online ad campaigns in a pipeline: causal models at scale (DC, RG, OG, TH, DL), pp. 7–16.
KDD-2010-TangAOM #empirical #framework #performance
Overlapping experiment infrastructure: more, better, faster experimentation (DT, AA, DO, MM), pp. 17–26.
KDD-2010-LiWZCMJ #performance
Exploitation and exploration in a performance based contextual advertising system (WL, XW, RZ, YC, JM, RJ), pp. 27–36.
KDD-2010-KarguptaSG #data mining #distributed #mining #overview #performance
MineFleet®: an overview of a widely adopted distributed vehicle performance data mining system (HK, KS, MG), pp. 37–46.
KDD-2010-DasMSO #algorithm #case study #detection #kernel #learning #multi #safety
Multiple kernel learning for heterogeneous anomaly detection: algorithm and aviation safety case study (SD, BLM, ANS, NCO), pp. 47–56.
KDD-2010-GoorhaU #roadmap
Discovery of significant emerging trends (SG, LHU), pp. 57–64.
KDD-2010-KumarGM #data mining #fault #health #mining #predict
Data mining to predict and prevent errors in health insurance claims processing (MK, RG, ZSM), pp. 65–74.
KDD-2010-AbeMPRJTBACKDG #learning #optimisation #using
Optimizing debt collections using constrained reinforcement learning (NA, PM, CP, CKR, DLJ, VPT, JJB, GFA, BRC, MK, MD, TG), pp. 75–84.
KDD-2010-CaoOYW #behaviour #detection #sequence
Detecting abnormal coupled sequences and sequence changes in group-based manipulative trading behaviors (LC, YO, PSY, GW), pp. 85–94.
KDD-2010-YeLCJ #automation #categorisation #clustering #using
Automatic malware categorization using cluster ensemble (YY, TL, YC, QJ), pp. 95–104.
KDD-2010-BozorgiSSV #heuristic #learning #predict
Beyond heuristics: learning to classify vulnerabilities and predict exploits (MB, LKS, SS, GMV), pp. 105–114.
KDD-2010-MaxwellBR #graph #memory management #mining #using
Diagnosing memory leaks using graph mining on heap dumps (EKM, GB, NR), pp. 115–124.
KDD-2010-ZhengSTLLCH #data mining #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.
KDD-2010-HoTL #learning #metric #reduction #sequence #similarity
Tropical cyclone event sequence similarity search via dimensionality reduction and metric learning (SSH, WT, WTL), pp. 135–144.
KDD-2010-BennetGLSV #benchmark #metric #named #scalability #towards
Malstone: towards a benchmark for analytics on large data clouds (CB, RLG, DL, JS, SV), pp. 145–152.
KDD-2010-WeiLSPZQSTZ #named #visual notation
TIARA: a visual exploratory text analytic system (FW, SL, YS, SP, MXZ, WQ, LS, LT, QZ), pp. 153–162.
KDD-2010-HendersonEFALMPT #approach #forensics #graph #metric #mining #multi
Metric forensics: a multi-level approach for mining volatile graphs (KH, TER, CF, LA, LL, KM, BAP, HT), pp. 163–172.
KDD-2010-WallaceSBT #learning
Active learning for biomedical citation screening (BCW, KS, CEB, TAT), pp. 173–182.
KDD-2010-KhoslaCLCHL #approach #machine learning #predict
An integrated machine learning approach to stroke prediction (AK, YC, CCYL, HKC, JH, HL), pp. 183–192.
KDD-2010-YanFDR #classification
Medical coding classification by leveraging inter-code relationships (YY, GF, JGD, RR), pp. 193–202.
KDD-2010-WangHJTZYG #mining #network #research
Mining advisor-advisee relationships from research publication networks (CW, JH, YJ, JT, DZ, YY, JG), pp. 203–212.
KDD-2010-AgarwalAKK #modelling #multi #scalability
Estimating rates of rare events with multiple hierarchies through scalable log-linear models (DA, RA, RK, NK), pp. 213–222.
KDD-2010-SrikantBWP #modelling
User browsing models: relevance versus examination (RS, SB, NW, DP), pp. 223–232.
KDD-2010-RothBDFHLLMM #graph #social #using
Suggesting friends using the implicit social graph (MR, ABD, DD, GF, IH, AL, NL, YM, RM), pp. 233–242.
KDD-2010-LichtenwalterLC #predict
New perspectives and methods in link prediction (RL, JTL, NVC), pp. 243–252.
KDD-2010-TsengWSY #algorithm #mining #named #performance
UP-Growth: an efficient algorithm for high utility itemset mining (VST, CWW, BES, PSY), pp. 253–262.
KDD-2010-Ruggieri #mining
Frequent regular itemset mining (SR), pp. 263–272.
KDD-2010-SunCCC #mining #nondeterminism #probability
Mining uncertain data with probabilistic guarantees (LS, RC, DWC, JC), pp. 273–282.
KDD-2010-LamC #data type #flexibility #mining
Mining top-k frequent items in a data stream with flexible sliding windows (HTL, TC), pp. 283–292.
KDD-2010-Tatti
Probably the best itemsets (NT), pp. 293–302.
KDD-2010-ZhuLX #feature model #incremental #learning #markov #named #performance #random
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields (JZ, NL, EPX), pp. 303–312.
KDD-2010-SunCY #approach #reduction #scalability
A scalable two-stage approach for a class of dimensionality reduction techniques (LS, BC, JY), pp. 313–322.
KDD-2010-LiuYY #algorithm #performance #problem
An efficient algorithm for a class of fused lasso problems (JL, LY, JY), pp. 323–332.
KDD-2010-CaiZH #clustering #feature model #multi
Unsupervised feature selection for multi-cluster data (DC, CZ, XH), pp. 333–342.
KDD-2010-YangO #feature model #predict #probability #using
Feature selection for support vector regression using probabilistic prediction (JBY, CJO), pp. 343–352.
KDD-2010-JinZZD #privacy
Versatile publishing for privacy preservation (XJ, MZ, NZ, GD), pp. 353–362.
KDD-2010-LinC #outsourcing #privacy #random
Privacy-preserving outsourcing support vector machines with random transformation (KPL, MSC), pp. 363–372.
KDD-2010-WenL #on the #quality #social
On the quality of inferring interests from social neighbors (ZW, CYL), pp. 373–382.
KDD-2010-SarangiM #named #nondeterminism #similarity
DUST: a generalized notion of similarity between uncertain time series (SRS, KM), pp. 383–392.
KDD-2010-LeroyCB #predict
Cold start link prediction (VL, BBC, FB), pp. 393–402.
KDD-2010-LiuZ #learning
Learning with cost intervals (XYL, ZHZ), pp. 403–412.
KDD-2010-AdaB #composition #generative
The new iris data: modular data generators (IA, MRB), pp. 413–422.
KDD-2010-AttenbergP #classification #learning #modelling #why
Why label when you can search?: alternatives to active learning for applying human resources to build classification models under extreme class imbalance (JA, FJP), pp. 423–432.
KDD-2010-FangNF
Discovering significant relaxed order-preserving submatrices (QF, WN, JF), pp. 433–442.
KDD-2010-HeP #topic
Topic dynamics: an alternative model of bursts in streams of topics (DH, DSPJ), pp. 443–452.
KDD-2010-SundaravaradanHSSVHR #biology #modelling
Extracting temporal signatures for comprehending systems biology models (NS, KSMTH, VS, DJS, JPCV, LSH, NR), pp. 453–462.
KDD-2010-LiLZ #algorithm #collaboration #concept #correlation
Negative correlations in collaboration: concepts and algorithms (JL, QL, TZ), pp. 463–472.
KDD-2010-TaiYC #mining #outsourcing #pseudo #taxonomy
k-Support anonymity based on pseudo taxonomy for outsourcing of frequent itemset mining (CHT, PSY, MSC), pp. 473–482.
KDD-2010-YangNSS #data mining #mining #privacy
Collusion-resistant privacy-preserving data mining (BY, HN, IS, JS), pp. 483–492.
KDD-2010-FriedmanS #data mining #difference #mining #privacy
Data mining with differential privacy (AF, AS), pp. 493–502.
KDD-2010-BhaskarLST
Discovering frequent patterns in sensitive data (RB, SL, AS, AT), pp. 503–512.
KDD-2010-SarkarM #graph #nearest neighbour #performance
Fast nearest-neighbor search in disk-resident graphs (PS, AWM), pp. 513–522.
KDD-2010-AlaeiKMV #representation
Balanced allocation with succinct representation (SA, RK, AM, EV), pp. 523–532.
KDD-2010-MaserratP #network #query #social
Neighbor query friendly compression of social networks (HM, JP), pp. 533–542.
KDD-2010-HeFLC #graph #parallel #scalability
Parallel SimRank computation on large graphs with iterative aggregation (GH, HF, CL, HC), pp. 543–552.
KDD-2010-KumarMM
Dynamics of conversations (RK, MM, MM), pp. 553–562.
KDD-2010-WangD #clustering #flexibility
Flexible constrained spectral clustering (XW, ID), pp. 563–572.
KDD-2010-DangB #clustering #linear
A hierarchical information theoretic technique for the discovery of non linear alternative clusterings (XHD, JB), pp. 573–582.
KDD-2010-BohmPSY #clustering
Clustering by synchronization (CB, CP, JS, QY), pp. 583–592.
KDD-2010-HossainTWDHR #clustering
Unifying dependent clustering and disparate clustering for non-homogeneous data (MSH, ST, LTW, ID, RFH, NR), pp. 593–602.
KDD-2010-MarchRG #algorithm #analysis #performance
Fast euclidean minimum spanning tree: algorithm, analysis, and applications (WBM, PR, AGG), pp. 603–612.
KDD-2010-LouFYLW #mining #workflow
Mining program workflow from interleaved traces (JGL, QF, SY, JL, BW), pp. 613–622.
KDD-2010-ShahafG
Connecting the dots between news articles (DS, CG), pp. 623–632.
KDD-2010-ZouGL #database #graph #nondeterminism #probability #semantics
Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics (ZZ, HG, JL), pp. 633–642.
KDD-2010-FeiH #classification #functional #graph
Boosting with structure information in the functional space: an application to graph classification (HF, JH), pp. 643–652.
KDD-2010-HuhF #learning #modelling #topic
Discriminative topic modeling based on manifold learning (SH, SEF), pp. 653–662.
KDD-2010-IwataYSU #modelling #multi #online #topic
Online multiscale dynamic topic models (TI, TY, YS, NU), pp. 663–672.
KDD-2010-SatoN #modelling #process #topic #using
Topic models with power-law using Pitman-Yor process (IS, HN), pp. 673–682.
KDD-2010-LuHCPHL #social #topic
The topic-perspective model for social tagging systems (CL, XH, XC, JrP, TH, ZL), pp. 683–692.
KDD-2010-JahrerTL #predict #recommendation
Combining predictions for accurate recommender systems (MJ, AT, RAL), pp. 693–702.
KDD-2010-AgarwalCE #learning #online #performance #recommendation
Fast online learning through offline initialization for time-sensitive recommendation (DA, BCC, PE), pp. 703–712.
KDD-2010-Steck #random #recommendation #testing
Training and testing of recommender systems on data missing not at random (HS), pp. 713–722.
KDD-2010-XiangYZCZYS #graph #recommendation
Temporal recommendation on graphs via long- and short-term preference fusion (LX, QY, SZ, LC, XZ, QY, JS), pp. 723–732.
KDD-2010-MiaoMYTCA #generative #modelling #network
Generative models for ticket resolution in expert networks (GM, LEM, XY, ST, YC, NA), pp. 733–742.
KDD-2010-Lee #classification #learning
Learning to combine discriminative classifiers: confidence based (CHL), pp. 743–752.
KDD-2010-LiAZ #mining
Mining positive and negative patterns for relevance feature discovery (YL, AA, NZ), pp. 753–762.
KDD-2010-YuHW #clustering #documentation #feature model #process
Document clustering via dirichlet process mixture model with feature selection (GY, RzH, ZW), pp. 763–772.
KDD-2010-ReichartzKP #dependence #kernel #semantics
Semantic relation extraction with kernels over typed dependency trees (FR, HK, GP), pp. 773–782.
KDD-2010-WangLZ #analysis #approach #overview #rating
Latent aspect rating analysis on review text data: a rating regression approach (HW, YL, CZ), pp. 783–792.
KDD-2010-KongY #classification #feature model #graph
Semi-supervised feature selection for graph classification (XK, PSY), pp. 793–802.
KDD-2010-DuBoisS #modelling #relational
Modeling relational events via latent classes (CD, PS), pp. 803–812.
KDD-2010-GaoLFWSH #community #detection #network #on the #performance
On community outliers and their efficient detection in information networks (JG, FL, WF, CW, YS, JH), pp. 813–822.
KDD-2010-PrestonBKSF #clustering #constraints #using
Redefining class definitions using constraint-based clustering: an application to remote sensing of the earth’s surface (DP, CEB, RK, DSM, MAF), pp. 823–832.
KDD-2010-YuHCL #classification #in memory #linear #memory management #scalability
Large linear classification when data cannot fit in memory (HFY, CJH, KWC, CJL), pp. 833–842.
KDD-2010-PrengerLVCH #bound #classification #fault
Class-specific error bounds for ensemble classifiers (RJP, TDL, KRV, BYC, WGH), pp. 843–852.
KDD-2010-RaykarKY #classification #design #performance #trade-off
Designing efficient cascaded classifiers: tradeoff between accuracy and cost (VCR, BK, SY), pp. 853–860.
KDD-2010-GaoW #mining #nondeterminism
Direct mining of discriminative patterns for classifying uncertain data (CG, JW), pp. 861–870.
KDD-2010-LuWZB
Ensemble pruning via individual contribution ordering (ZL, XW, XZ, JB), pp. 871–880.
KDD-2010-LaoC #execution #modelling #performance #query #random #retrieval
Fast query execution for retrieval models based on path-constrained random walks (NL, WWC), pp. 881–888.
KDD-2010-ChuaL #generative #modelling #network #online #rating #trust #using
Trust network inference for online rating data using generative models (FCTC, EPL), pp. 889–898.
KDD-2010-GeXTXGP #energy #mobile #recommendation
An energy-efficient mobile recommender system (YG, HX, AT, KX, MG, MJP), pp. 899–908.
KDD-2010-SomaiyaJR #learning #modelling
Mixture models for learning low-dimensional roles in high-dimensional data (MS, CMJ, SR), pp. 909–918.
KDD-2010-LiuLNFL #clustering #towards
Towards mobility-based clustering (SL, YL, LMN, JF, ML), pp. 919–928.
KDD-2010-LinZMH #community #named #social #statistics
PET: a statistical model for popular events tracking in social communities (CXL, BZ, QM, JH), pp. 929–938.
KDD-2010-SozioG #how #problem
The community-search problem and how to plan a successful cocktail party (MS, AG), pp. 939–948.
KDD-2010-PlangprasopchokLG #folksonomy #metadata
Growing a tree in the forest: constructing folksonomies by integrating structured metadata (AP, KL, LG), pp. 949–958.
KDD-2010-YinXHD #personalisation #predict #probability
A probabilistic model for personalized tag prediction (DY, ZX, LH, BDD), pp. 959–968.
KDD-2010-LiuNYW #automation #named #wiki
BioSnowball: automated population of Wikis (XL, ZN, NY, JRW), pp. 969–978.
KDD-2010-Sculley #ranking
Combined regression and ranking (DS), pp. 979–988.
KDD-2010-TingZLT #estimation
Mass estimation and its applications (KMT, GTZ, FTL, JSCT), pp. 989–998.
KDD-2010-ZhangZ #dependence #learning #multi
Multi-label learning by exploiting label dependency (MLZ, KZ), pp. 999–1008.
KDD-2010-MeiGR #named #network
DivRank: the interplay of prestige and diversity in information networks (QM, JG, DRR), pp. 1009–1018.
KDD-2010-Gomez-RodriguezLK #network
Inferring networks of diffusion and influence (MGR, JL, AK), pp. 1019–1028.
KDD-2010-ChenWW #network #scalability #social
Scalable influence maximization for prevalent viral marketing in large-scale social networks (WC, CW, YW), pp. 1029–1038.
KDD-2010-WangCSX #algorithm #mining #mobile #network #social
Community-based greedy algorithm for mining top-K influential nodes in mobile social networks (YW, GC, GS, KX), pp. 1039–1048.
KDD-2010-TanTSLW #graph #social
Social action tracking via noise tolerant time-varying factor graphs (CT, JT, JS, QL, FW), pp. 1049–1058.
KDD-2010-LappasTGM #network #social
Finding effectors in social networks (TL, ET, DG, HM), pp. 1059–1068.
KDD-2010-ChenLB #approach #detection #named #statistics
GLS-SOD: a generalized local statistical approach for spatial outlier detection (FC, CTL, APB), pp. 1069–1078.
KDD-2010-ZhangSZL #corpus #correlation #multi #process
Evolutionary hierarchical dirichlet processes for multiple correlated time-varying corpora (JZ, YS, CZ, SL), pp. 1079–1088.
KDD-2010-MueenK #maintenance #online
Online discovery and maintenance of time series motifs (AM, EJK), pp. 1089–1098.
KDD-2010-LiDHKN #behaviour #mining
Mining periodic behaviors for moving objects (ZL, BD, JH, RK, PN), pp. 1099–1108.
KDD-2010-WangC #algorithm #linear #modelling #performance
An efficient causal discovery algorithm for linear models (ZW, LC), pp. 1109–1118.
KDD-2010-DurrantK #analysis #classification #linear
Compressed fisher linear discriminant analysis: classification of randomly projected data (RJD, AK), pp. 1119–1128.
KDD-2010-HeLC #kernel #scalability #similarity
Scalable similarity search with optimized kernel hashing (JH, WL, SFC), pp. 1129–1138.
KDD-2010-LiuMTLL #learning #metric #optimisation #using
Semi-supervised sparse metric learning using alternating linearization optimization (WL, SM, DT, JL, PL), pp. 1139–1148.
KDD-2010-AgarwalPV #multi #scalability
Universal multi-dimensional scaling (AA, JMP, SV), pp. 1149–1158.
KDD-2010-YangJJZT #categorisation #classification
Unsupervised transfer classification: application to text categorization (TY, RJ, AKJ, YZ, WT), pp. 1159–1168.
KDD-2010-GuptaPATV #learning #retrieval #social #social media
Nonnegative shared subspace learning and its application to social media retrieval (SKG, DQP, BA, TT, SV), pp. 1169–1178.
KDD-2010-ChenLY #learning #multi #rank
Learning incoherent sparse and low-rank patterns from multiple tasks (JC, JL, JY), pp. 1179–1188.
KDD-2010-ChapelleSVWZT #learning #multi #ranking #web
Multi-task learning for boosting with application to web search ranking (OC, PKS, SV, KQW, YZ, BLT), pp. 1189–1198.
KDD-2010-ZhangY #learning #metric
Transfer metric learning by learning task relationships (YZ, DYY), pp. 1199–1208.
KDD-2010-KarguptaGF #data mining #generative #mining
The next generation of transportation systems, greenhouse emissions, and data mining (HK, JG, WF), pp. 1209–1212.

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