Stem frequent$ (all stems)

182 papers:

LATA-2015-ChoHK #mining

Frequent Pattern Mining with Non-overlapping Inversions (DJC, YSH, HK), pp. 121–132.

KDD-2015-RiondatoU #mining

Mining Frequent Itemsets through Progressive Sampling with Rademacher Averages (MR, EU), pp. 1005–1014.

MLDM-2015-SalahAM #mining #optimisation #performance #pipes and filters

Optimizing the Data-Process Relationship for Fast Mining of Frequent Itemsets in MapReduce (SS, RA, FM), pp. 217–231.

CGO-2015-NagarajG #analysis #approximate #mining #pointer #using

Approximating flow-sensitive pointer analysis using frequent itemset mining (VN, RG), pp. 225–234.

SIGMOD-2014-YamamotoIF #approximate #data type #mining

Resource-oriented approximation for frequent itemset mining from bursty data streams (YY, KI, SF), pp. 205–216.

VLDB-2014-ElseidyASK #graph #mining #named #scalability

GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph (ME, EA, SS, PK), pp. 517–528.

VLDB-2015-ThirumuruganathanRAD14 #mining

Beyond Itemsets: Mining Frequent Featuresets over Structured Items (ST, HR, SA, GD), pp. 257–268.

CSMR-WCRE-2014-OsmanLN #debugging #mining

Mining frequent bug-fix code changes (HO, ML, ON), pp. 343–347.

ICEIS-v1-2014-ChinoGRTT #named #scalability

TrieMotif — A New and Efficient Method to Mine Frequent K-Motifs from Large Time Series (DYTC, RRdVG, LASR, CTJ, AJMT), pp. 60–69.

CIKM-2014-LimCK #data type #performance

Fast, Accurate, and Space-efficient Tracking of Time-weighted Frequent Items from Data Streams (YL, JC, UK), pp. 1109–1118.

CIKM-2014-LiuXD #mining #network #predict

Relationship Emergence Prediction in Heterogeneous Networks through Dynamic Frequent Subgraph Mining (YL, SX, LD), pp. 1649–1658.

KDD-2014-LeeC

Top-k frequent itemsets via differentially private FP-trees (JL, CWC), pp. 931–940.

KDIR-2014-AlHuwaishelAB #algorithm #case study #database

Finding the Frequent Pattern in a Database — A Study on the Apriori Algorithm (NA, MA, GB), pp. 388–396.

SIGIR-2014-JiangA #query

Necessary and frequent terms in queries (JJ, JA), pp. 1167–1170.

SIGIR-2014-WangZZL #identification #keyword #twitter

Efficiently identify local frequent keyword co-occurrence patterns in geo-tagged Twitter stream (XW, YZ, WZ, XL), pp. 1215–1218.

PODS-2013-Gottlob #identification #polynomial

Deciding monotone duality and identifying frequent itemsets in quadratic logspace (GG), pp. 25–36.

PODS-2013-KimelfeldK #complexity #mining

The complexity of mining maximal frequent subgraphs (BK, PGK), pp. 13–24.

SIGMOD-2013-LuoT0N

Finding time period-based most frequent path in big trajectory data (WL, HT, LC, LMN), pp. 713–724.

SIGMOD-2013-MiliarakiBGZ #mining #scalability #sequence

Mind the gap: large-scale frequent sequence mining (IM, KB, RG, SZ), pp. 797–808.

VLDB-2013-Bonomi #difference #mining #privacy

Mining Frequent Patterns with Differential Privacy (LB), pp. 1422–1427.

VLDB-2013-LiuSW #performance #query

A Performance Study of Three Disk-based Structures for Indexing and Querying Frequent Itemsets (GL, AS, LW), pp. 505–516.

CIKM-2013-HanW #graph #mining #scalability

Mining frequent neighborhood patterns in a large labeled graph (JH, JRW), pp. 259–268.

KDD-2013-LiuCZ #approach #performance #probability

Summarizing probabilistic frequent patterns: a fast approach (CL, LC, CZ), pp. 527–535.

KDD-2013-ShenY #difference #graph #mining #privacy

Mining frequent graph patterns with differential privacy (ES, TY), pp. 545–553.

MLDM-2013-CarvalhoR #bibliography #nondeterminism #perspective

Discovering Frequent Itemsets on Uncertain Data: A Systematic Review (JVdC, DDR), pp. 390–404.

SAC-2013-CameronCL #memory management #mining #set

Stream mining of frequent sets with limited memory (JJC, AC, CKSL), pp. 173–175.

SAC-2013-KumarR #algorithm #data type #identification #online

Online identification of frequently executed acyclic paths by leveraging data stream algorithms (GK, SR), pp. 1694–1695.

SAC-2013-ZhangHMZMM #data type #mining

Mining frequent itemsets over tuple-evolving data streams (CZ, YH, MM, CZ, HM, FM), pp. 267–274.

ICSE-2013-BettenburgB #development #mining

Deciphering the story of software development through frequent pattern mining (NB, AB), pp. 1197–1200.

VLDB-2012-AhmadKKN #higher-order #named

DBToaster: Higher-order Delta Processing for Dynamic, Frequently Fresh Views (YA, OK, CK, MN), pp. 968–979.

VLDB-2012-LiQSC #difference #mining #named #privacy

PrivBasis: Frequent Itemset Mining with Differential Privacy (NL, WHQ, DS, JC), pp. 1340–1351.

VLDB-2012-TongCCY #database #mining #nondeterminism

Mining Frequent Itemsets over Uncertain Databases (YT, LC, YC, PSY), pp. 1650–1661.

VLDB-2013-ZengNC12 #mining #on the

On differentially private frequent itemset mining (CZ, JFN, JYC), pp. 25–36.

CIKM-2012-BonomiXCF #privacy

Frequent grams based embedding for privacy preserving record linkage (LB, LX, RC, BCMF), pp. 1597–1601.

CIKM-2012-KozawaAK #database #gpu #mining #nondeterminism #probability

GPU acceleration of probabilistic frequent itemset mining from uncertain databases (YK, TA, HK), pp. 892–901.

CIKM-2012-TangZLW #mining #quality #recommendation

Incorporating occupancy into frequent pattern mining for high quality pattern recommendation (LT, LZ, PL, MW), pp. 75–84.

ICPR-2012-MarcaciniCR #approach #clustering #learning

An active learning approach to frequent itemset-based text clustering (RMM, GNC, SOR), pp. 3529–3532.

KDD-2012-LiZ

Sampling minimal frequent boolean (DNF) patterns (GL, MJZ), pp. 87–95.

KDD-2012-RoyTA #hardware #manycore #performance

Efficient frequent item counting in multi-core hardware (PR, JT, GA), pp. 1451–1459.

KDD-2012-TongCY #mining #named #nondeterminism

UFIMT: an uncertain frequent itemset mining toolbox (YT, LC, PSY), pp. 1508–1511.

KDIR-2012-ArbelaitzGLMPP #adaptation #clustering #mining #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.

KDIR-2012-QuirogaMH #sequence

Frequent and Significant Episodes in Sequences of Events — Computation of a New Frequency Measure based on Individual Occurrences of the Events (OQ, JM, SH), pp. 324–328.

KDIR-2012-Vanetik #classification #dataset

Classification of Datasets with Frequent Itemsets is Wild (NV), pp. 386–389.

SAC-2012-BaralisCJF #multi #summary

Multi-document summarization exploiting frequent itemsets (EB, LC, SJ, AF), pp. 782–786.

SAC-2012-LeungS #constraints #mining

A new class of constraints for constrained frequent pattern mining (CKSL, LS), pp. 199–204.

SIGMOD-2011-CaoSSYDGW #algorithm #consistency #performance

Fast checkpoint recovery algorithms for frequently consistent applications (TC, MAVS, BS, YY, AJD, JG, WMW), pp. 265–276.

CHI-2011-PollakAG #metric #named

PAM: a photographic affect meter for frequent, in situ measurement of affect (JPP, PA, GG), pp. 725–734.

ICEIS-J-2011-WangN11a #mining #named #nondeterminism

UF-Evolve: Uncertain Frequent Pattern Mining (SW, VTYN), pp. 98–116.

ICEIS-v1-2011-WangN #mining #named #nondeterminism

UF-Evolve — Uncertain Frequent Pattern Mining (SW, VTYN), pp. 74–84.

CIKM-2011-GuoZTG #data type #mining #multi

Mining frequent patterns across multiple data streams (JG, PZ, JT, LG), pp. 2325–2328.

KDD-2011-BifetHPG #data type #evolution #graph #mining

Mining frequent closed graphs on evolving data streams (AB, GH, BP, RG), pp. 591–599.

KDIR-2011-HubwieserM #declarative #named #object-oriented

Knowpats: Patterns of Declarative Knowledge — Searching Frequent Knowledge Patterns about Object-orientation (PH, AM), pp. 358–364.

KDIR-2011-OmerBG #algorithm #mining #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.

KDIR-2011-VanetikG #mining #named

HashMax: A New Method for Mining Maximal Frequent Itemsets (NV, EG), pp. 140–145.

MLDM-2011-Kessl #mining #parallel #using

Static Load Balancing of Parallel Mining of Frequent Itemsets Using Reservoir Sampling (RK), pp. 553–567.

SAC-2011-LeungJ #data type #mining #nondeterminism #using

Frequent itemset mining of uncertain data streams using the damped window model (CKSL, FJ), pp. 950–955.

SAC-2011-LeungS #equivalence #mining #nondeterminism

Equivalence class transformation based mining of frequent itemsets from uncertain data (CKSL, LS), pp. 983–984.

CIKM-2010-WangCLC #approach #mining #modelling #probability

Accelerating probabilistic frequent itemset mining: a model-based approach (LW, RC, SDL, DWLC), pp. 429–438.

KDD-2010-BhaskarLST

Discovering frequent patterns in sensitive data (RB, SL, AS, AT), pp. 503–512.

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-Ruggieri #mining

Frequent regular itemset mining (SR), pp. 263–272.

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-ZouGL #database #graph #nondeterminism #probability #semantics

Discovering frequent subgraphs over uncertain graph databases under probabilistic semantics (ZZ, HG, JL), pp. 633–642.

KDIR-2010-GokceA #algorithm #trade-off

A Tradeoff Balancing Algorithm for Hiding Sensitive Frequent Itemsets (HG, OA), pp. 200–205.

KDIR-2010-JedrzejczakW #generative #query #using

Integrated Candidate Generation in Processing Batches of Frequent Itemset Queries using Apriori (PJ, MW), pp. 487–490.

KDIR-2010-ZakourSMM #constraints

Time Constraints Extension on Frequent Sequential Patterns (ABZ, MS, SM, MM), pp. 281–287.

SAC-2010-LeungHB #constraints #mining #nondeterminism

Mining uncertain data for frequent itemsets that satisfy aggregate constraints (CKSL, BH, DAB), pp. 1034–1038.

DAC-2009-PatilLZWM #logic #using

Digital VLSI logic technology using Carbon Nanotube FETs: frequently asked questions (NP, AL, JZ, HSPW, SM), pp. 304–309.

PODS-2009-KirschMPPUV #approach #identification #performance #statistics

An efficient rigorous approach for identifying statistically significant frequent itemsets (AK, MM, AP, GP, EU, FV), pp. 117–126.

VLDB-2009-LeglerLSK #distributed #mining #robust #using

Robust Distributed Top-N Frequent Pattern Mining Using the SAP BW Accelerator (TL, WL, JS, JK), pp. 1438–1449.

VLDB-2009-WongCHKM #mining #outsourcing

An Audit Environment for Outsourcing of Frequent Itemset Mining (WKW, DWLC, EH, BK, NM), pp. 1162–1172.

ICEIS-J-2009-NguyenH #approach #documentation

Frequent Subgraph-Based Approach for Classifying Vietnamese Text Documents (TANH, KH), pp. 299–308.

CIKM-2009-TaoO09a #data type #mining

Mining frequent itemsets in time-varying data streams (YT, MTÖ), pp. 1521–1524.

CIKM-2009-ZouLGZ #graph #mining #nondeterminism

Frequent subgraph pattern mining on uncertain graph data (ZZ, JL, HG, SZ), pp. 583–592.

KDD-2009-AggarwalLWW #mining #nondeterminism

Frequent pattern mining with uncertain data (CCA, YL, JW, JW), pp. 29–38.

KDD-2009-BerneckerKRVZ #database #mining #nondeterminism #probability

Probabilistic frequent itemset mining in uncertain databases (TB, HPK, MR, FV, AZ), pp. 119–128.

KDD-2009-JinXL #representation #set

Cartesian contour: a concise representation for a collection of frequent sets (RJ, YX, LL), pp. 417–426.

KDD-2009-PoernomoG #named #representation

CP-summary: a concise representation for browsing frequent itemsets (AKP, VG), pp. 687–696.

KDD-2009-PoernomoG09a #fault tolerance #mining #performance #towards

Towards efficient mining of proportional fault-tolerant frequent itemsets (AKP, VG), pp. 697–706.

KDIR-2009-KiranR #approach

An Improved Frequent Pattern-growth Approach to Discover Rare Association Rules (RUK, PKR), pp. 43–52.

MLDM-2009-CeciALCFVM #data type #detection #mining #relational

Relational Frequent Patterns Mining for Novelty Detection from Data Streams (MC, AA, CL, CC, FF, CV, DM), pp. 427–439.

SAC-2009-LeonardiORRS

Frequent spatio-temporal patterns in trajectory data warehouses (LL, SO, AR, AR, CS), pp. 1433–1440.

SIGMOD-2008-ZhangLY #probability

Finding frequent items in probabilistic data (QZ, FL, KY), pp. 819–832.

VLDB-2008-CormodeH #data type

Finding frequent items in data streams (GC, MH), pp. 1530–1541.

CIKM-2008-TanbeerAJL #data type #mining #performance

Efficient frequent pattern mining over data streams (SKT, CFA, BSJ, YKL), pp. 1447–1448.

KDD-2008-BifetG #adaptation #data type #mining

Mining adaptively frequent closed unlabeled rooted trees in data streams (AB, RG), pp. 34–42.

KDD-2008-FanZCGYHYV #mining #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.

KDD-2008-GuptaFFSK #algorithm #approximate #evaluation #mining

Quantitative evaluation of approximate frequent pattern mining algorithms (RG, GF, BF, MS, VK), pp. 301–309.

KDD-2008-LaxmanTW #generative #modelling #predict #sequence #using

Stream prediction using a generative model based on frequent episodes in event sequences (SL, VT, RWW), pp. 453–461.

SEKE-2008-DingH #algorithm #mining #named #performance

VP: an Efficient Algorithm for Frequent Itemset Mining (QD, WSH), pp. 381–386.

SAC-2008-LaRosaXM #kernel #mining

Frequent pattern mining for kernel trace data (CL, LX, KM), pp. 880–885.

SAC-2008-ZengLL #fault tolerance #mining

Mining fault-tolerant frequent patterns efficiently with powerful pruning (JJZ, GL, CCL), pp. 927–931.

PPoPP-2008-TatikondaP #adaptation #approach #architecture #manycore #memory management #mining

An adaptive memory conscious approach for mining frequent trees: implications for multi-core architectures (ST, SP), pp. 263–264.

VLDB-2007-LiL #mining #multi #optimisation

Optimization of Frequent Itemset Mining on Multiple-Core Processor (EL, LL), pp. 1275–1285.

KDD-2007-LaxmanSU #algorithm #performance

A fast algorithm for finding frequent episodes in event streams (SL, PSS, KPU), pp. 410–419.

KDD-2007-YuanWY #semantics #visual notation

From frequent itemsets to semantically meaningful visual patterns (JY, YW, MY), pp. 864–873.

MLDM-2007-AoYHH #data type #mining

Mining Maximal Frequent Itemsets in Data Streams Based on FP-Tree (FA, YY, JH, KH), pp. 479–489.

MLDM-2007-FullerK #data type #distributed #monitoring #named

FIDS: Monitoring Frequent Items over Distributed Data Streams (RF, MMK), pp. 464–478.

MLDM-2007-Morzy #mining #predict

Mining Frequent Trajectories of Moving Objects for Location Prediction (MM), pp. 667–680.

SAC-2007-Knijf #mining #named

FAT-miner: mining frequent attribute trees (JDK), pp. 417–422.

SAC-2007-LianCY #database #maintenance #scalability

Maintenance of maximal frequent itemsets in large databases (WL, DWC, SMY), pp. 388–392.

PODS-2006-LeeT #performance

A simpler and more efficient deterministic scheme for finding frequent items over sliding windows (LKL, HFT), pp. 290–297.

SIGMOD-2006-XiaZ #performance

Refreshing the sky: the compressed skycube with efficient support for frequent updates (TX, DZ), pp. 491–502.

VLDB-2006-JiTT #3d #dataset #mining

Mining Frequent Closed Cubes in 3D Datasets (LJ, KLT, AKHT), pp. 811–822.

CIKM-2006-Gkoulalas-DivanisV #approach #integer #programming

An integer programming approach for frequent itemset hiding (AGD, VSV), pp. 748–757.

CIKM-2006-KunkleZC #mining #performance

Efficient mining of max frequent patterns in a generalized environment (DK, DZ, GC), pp. 810–811.

ICPR-v1-2006-BauerBSP #sequence #video

Finding Highly Frequented Paths in Video Sequences (DB, NB, SS, RPP), pp. 387–391.

KDD-2006-BuehrerPG #mining

Out-of-core frequent pattern mining on a commodity PC (GB, SP, AG), pp. 86–95.

KDD-2006-HorvathRW #graph #mining

Frequent subgraph mining in outerplanar graphs (TH, JR, SW), pp. 197–206.

KDD-2006-JiangG #data type #mining #named

CFI-Stream: mining closed frequent itemsets in data streams (NJ, LG), pp. 592–597.

KDD-2006-MeiXCHZ #analysis #generative #semantics

Generating semantic annotations for frequent patterns with context analysis (QM, DX, HC, JH, CZ), pp. 337–346.

SAC-2006-DextersPG #algorithm #analysis #probability

A probability analysis for candidate-based frequent itemset algorithms (ND, PWP, DVG), pp. 541–545.

VLDB-2005-GhotingBPKNCD #mining

Cache-conscious Frequent Pattern Mining on a Modern Processor (AG, GB, SP, DK, ADN, YKC, PD), pp. 577–588.

VLDB-2005-XinHYC #mining #set

Mining Compressed Frequent-Pattern Sets (DX, JH, XY, HC), pp. 709–720.

IWPC-2005-BeyerN #clustering

Clustering Software Artifacts Based on Frequent Common Changes (DB, AN), pp. 259–268.

CIKM-2005-Ahonen-Myka #mining #sequence #set #word

Mining all maximal frequent word sequences in a set of sentences (HAM), pp. 255–256.

CIKM-2005-ChuangC #constraints #memory management

Frequent pattern discovery with memory constraint (KTC, MSC), pp. 345–346.

CIKM-2005-JijkounR #web

Retrieving answers from frequently asked questions pages on the web (VJ, MdR), pp. 76–83.

CIKM-2005-LaurNSP #data type #estimation #on the

On the estimation of frequent itemsets for data streams: theory and experiments (PAL, RN, JES, PP), pp. 327–328.

CIKM-2005-LiC #clustering #documentation #sequence #word

Text document clustering based on frequent word sequences (YL, SMC), pp. 293–294.

CIKM-2005-LinS

Handling frequent updates of moving objects (BL, JS), pp. 493–500.

KDD-2005-JinWPPA #dataset #graph

Discovering frequent topological structures from graph datasets (RJ, CW, DP, SP, GA), pp. 606–611.

KDD-2005-Petrushin #mining #multi #self #using #video

Mining rare and frequent events in multi-camera surveillance video using self-organizing maps (VAP), pp. 794–800.

MLDM-2005-LaurSNP #data type #statistics

Statistical Supports for Frequent Itemsets on Data Streams (PAL, JES, RN, PP), pp. 395–404.

SAC-2005-EbertG #approach

A “Go With the Winners” approach to finding frequent patterns (TE, DG), pp. 498–502.

SAC-2005-ShangS #database #mining #relational

Depth-first frequent itemset mining in relational databases (XS, KUS), pp. 1112–1117.

SAC-2005-SilvestriO #approximate #distributed #mining

Distributed approximate mining of frequent patterns (CS, SO), pp. 529–536.

SAC-2005-SongR #transaction

Finding frequent itemsets by transaction mapping (MS, SR), pp. 488–492.

DATE-v1-2004-ZhangYV

Low Static-Power Frequent-Value Data Caches (CZ, JY, FV), pp. 214–219.

SIGMOD-2004-YanYH #approach #graph

Graph Indexing: A Frequent Structure-based Approach (XY, PSY, JH), pp. 335–346.

VLDB-2004-LiM

Computing Frequent Itemsets Inside Oracle 10G (WL, AM), pp. 1253–1256.

VLDB-2004-YuCLZ #data type #mining #transaction

False Positive or False Negative: Mining Frequent Itemsets from High Speed Transactional Data Streams (JXY, ZC, HL, AZ), pp. 204–215.

SCAM-2004-WahlerSGF #clone detection #detection #source code

Clone Detection in Source Code by Frequent Itemset Techniques (VW, DS, JWvG, GF), pp. 128–135.

CIKM-2004-ZhaoBMK #xml

Discovering frequently changing structures from historical structural deltas of unordered XML (QZ, SSB, MKM, YK), pp. 188–197.

KDD-2004-AfratiGM #approximate #set

Approximating a collection of frequent sets (FNA, AG, HM), pp. 12–19.

KDD-2004-HuanWPY #database #graph #mining #named

SPIN: mining maximal frequent subgraphs from graph databases (JH, WW, JP, JY), pp. 581–586.

KDD-2004-JaroszewiczS #network #using

Interestingness of frequent itemsets using Bayesian networks as background knowledge (SJ, DAS), pp. 178–186.

KDD-2004-NijssenK #difference #mining

A quickstart in frequent structure mining can make a difference (SN, JNK), pp. 647–652.

KDD-2004-Yang #complexity #mining

The complexity of mining maximal frequent itemsets and maximal frequent patterns (GY), pp. 344–353.

KDD-2004-YangLHG #mining #named #performance #query #xml

2PXMiner: an efficient two pass mining of frequent XML query patterns (LHY, MLL, WH, XG), pp. 731–736.

SAC-2004-Goethals #memory management #mining

Memory issues in frequent itemset mining (BG), pp. 530–534.

SAC-2004-Kryszkiewicz

Reducing borders of k-disjunction free representations of frequent patterns (MK), pp. 559–563.

SAC-2004-RuckertK #graph

Frequent free tree discovery in graph data (UR, SK), pp. 564–570.

SAC-2004-ShangSG #generative #mining #sql

SQL based frequent pattern mining without candidate generation (XS, KUS, IG), pp. 618–619.

PODS-2003-CormodeM #what

What’s hot and what’s not: tracking most frequent items dynamically (GC, SM), pp. 296–306.

VLDB-2003-LeeHJT #approach #bottom-up

Supporting Frequent Updates in R-Trees: A Bottom-Up Approach (MLL, WH, CSJ, BC, KLT), pp. 608–619.

ICEIS-v2-2003-SrikumarB #algorithm #mining #set #transaction

An Algorithm for Mining Maximal Frequent Sets Based on Dominancy of Transactions (KS, BB), pp. 422–425.

CIKM-2003-ChangL #adaptation #data type #monitoring #named #online

estWin: adaptively monitoring the recent change of frequent itemsets over online data streams (JHC, WSL), pp. 536–539.

CIKM-2003-JinQSYZ #data type #maintenance

Dynamically maintaining frequent items over a data stream (CJ, WQ, CS, JXY, AZ), pp. 287–294.

KDD-2003-ChangL #adaptation #data type #online

Finding recent frequent itemsets adaptively over online data streams (JHC, WSL), pp. 487–492.

KDD-2003-El-HajjZ #dataset #interactive #matrix #mining #performance #scalability

Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining (MEH, ORZ), pp. 109–118.

KDD-2003-LiuLLY #on the #query

On computing, storing and querying frequent patterns (GL, HL, WL, JXY), pp. 607–612.

KDD-2003-SheCWEGB #predict

Frequent-subsequence-based prediction of outer membrane proteins (RS, FC, KW, ME, JLG, FSLB), pp. 436–445.

KDD-2003-WangHP #mining

CLOSET+: searching for the best strategies for mining frequent closed itemsets (JW, JH, JP), pp. 236–245.

KDD-2003-YanH #graph #mining #named

CloseGraph: mining closed frequent graph patterns (XY, JH), pp. 286–295.

SAC-2003-JiaPP #constraints #mining

Tough Constraint-Based Frequent Closed Itemsets Mining (LJ, RP, DP), pp. 416–420.

SAC-2003-Lin #mining

Mining Maximal Frequent Intervals (JLL), pp. 426–431.

ICALP-2002-CharikarCF #data type

Finding Frequent Items in Data Streams (MC, KCC, MFC), pp. 693–703.

KDD-2002-BeilEX #clustering

Frequent term-based text clustering (FB, ME, XX), pp. 436–442.

KDD-2002-LiuPWH #mining #set

Mining frequent item sets by opportunistic projection (JL, YP, KW, JH), pp. 229–238.

KDD-2002-Zaki #mining

Efficiently mining frequent trees in a forest (MJZ), pp. 71–80.

SEKE-2002-VanT #collaboration #education #evaluation #named #process

FAQshare: a frequently asked questions voting system as a collaboration and evaluation tool in teaching activities (HLV, AT), pp. 557–560.

PODS-2001-BykowskiR #representation

A condensed representation to find frequent patterns (AB, CR).

VLDB-2001-StanoiRAA #database #set

Discovery of Influence Sets in Frequently Updated Databases (IS, MR, DA, AEA), pp. 99–108.

ITiCSE-2001-VanT #evaluation #optimisation #student

A “frequently asked questions” management system that supports voting, built for student evaluation and optimization purposes (HLV, AT), p. 184.

KDD-2001-Morimoto #database #mining #set

Mining frequent neighboring class sets in spatial databases (YM), pp. 353–358.

KDD-2001-YangFB #performance

Efficient discovery of error-tolerant frequent itemsets in high dimensions (CY, UMF, PSB), pp. 194–203.

KDD-T-2001-HanLP #bibliography #mining #perspective #scalability

Scalable frequent-pattern mining methods: an overview (JH, LVSL, JP), pp. 264–324.

SIGMOD-2000-HanPY #generative #mining

Mining Frequent Patterns without Candidate Generation (JH, JP, YY), pp. 1–12.

SIGMOD-2000-PeiMHZ #benchmark #data mining #metric #mining #performance #towards

Towards Data Mining Benchmarking: A Testbed for Performance Study of Frequent Pattern Mining (JP, RM, KH, HZ), p. 592.

VLDB-2000-WangHH #constraints #mining #using

Mining Frequent Itemsets Using Support Constraints (KW, YH, JH), pp. 43–52.

KDD-2000-HanPMCDH #mining #named

FreeSpan: frequent pattern-projected sequential pattern mining (JH, JP, BMA, QC, UD, MH), pp. 355–359.

KDD-2000-PeiH #constraints #mining #question

Can we push more constraints into frequent pattern mining? (JP, JH), pp. 350–354.

ASPLOS-2000-ZhangYG #design #locality

Frequent Value Locality and Value-Centric Data Cache Design (YZ, JY, RG), pp. 150–159.

CL-2000-BastidePTSL #mining #using

Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets (YB, NP, RT, GS, LL), pp. 972–986.

SIGMOD-1999-LakshmananNHP #constraints #optimisation #query #set

Optimization of Constrained Frequent Set Queries with 2-variable Constraints (LVSL, RTN, JH, AP), pp. 157–168.

SIGMOD-1999-NgLHM #mining #query #set

Exploratory Mining via Constrained Frequent Set Queries (RTN, LVSL, JH, TM), pp. 556–558.

TOOLS-ASIA-1999-LinLZZ #web

Efficiently Computing Frequent Tree-Like Topology Patterns in a Web Environment (XL, CL, YZ, XZ), pp. 440–447.

CIKM-1998-Zaki #performance #sequence

Efficient Enumeration of Frequent Sequences (MJZ), pp. 68–75.

KDD-1998-DehaspeTK

Finding Frequent Substructures in Chemical Compounds (LD, HT, RDK), pp. 30–36.

KDD-1996-MannilaT96a #multi #set

Multiple Uses of Frequent Sets and Condensed Representations (Extended Abstract) (HM, HT), pp. 189–194.

KDD-1995-MannilaTV #sequence

Discovering Frequent Episodes in Sequences (HM, HT, AIV), pp. 210–215.