Stem itemset$ (all stems)

96 papers:

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.

SAC-2015-Fournier-VigerZ #mining #named #performance

FOSHU: faster on-shelf high utility itemset mining — with or without negative unit profit (PFV, SZ), pp. 857–864.

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-2015-ThirumuruganathanRAD14 #mining

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

ICEIS-v1-2014-HenriquesA #database #generative #modelling #sequence

Generative Modeling of Itemset Sequences Derived from Real Databases (RH, CA), pp. 264–272.

KDD-2014-LeeC

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

KDIR-2014-BenhamouJSS #mining #symmetry

Symmetry Breaking in Itemset Mining (BB, SJ, LS, YS), pp. 86–96.

PODS-2013-Gottlob #identification #polynomial

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

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.

MLDM-2013-CarvalhoR #bibliography #nondeterminism #perspective

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

SAC-2013-ZhangHMZMM #data type #mining

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

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

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

CIKM-2012-LiuQ #generative #mining

Mining high utility itemsets without candidate generation (ML, JFQ), pp. 55–64.

ICPR-2012-MarcaciniCR #approach #clustering #learning

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

KDD-2012-TongCY #mining #named #nondeterminism

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

KDD-2012-WuSTY #mining

Mining top-K high utility itemsets (CWW, BES, VST, PSY), pp. 78–86.

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.

KDD-2011-MampaeyTV #what

Tell me what i need to know: succinctly summarizing data with itemsets (MM, NT, JV), pp. 573–581.

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-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-Tatti

Probably the best itemsets (NT), pp. 293–302.

KDD-2010-TsengWSY #algorithm #mining #named #performance

UP-Growth: an efficient algorithm for high utility itemset mining (VST, CWW, BES, PSY), pp. 253–262.

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.

SAC-2010-BaralisCC #dataset #mining #persistent #scalability

A persistent HY-Tree to efficiently support itemset mining on large datasets (EB, TC, SC), pp. 1060–1064.

SAC-2010-LeungHB #constraints #mining #nondeterminism

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

SAC-2010-ShieTY #data type #mining #online

Online mining of temporal maximal utility itemsets from data streams (BES, VST, PSY), pp. 1622–1626.

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-WongCHKM #mining #outsourcing

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

CIKM-2009-GaoW #generative #performance

Efficient itemset generator discovery over a stream sliding window (CG, JW), pp. 355–364.

CIKM-2009-TaoO09a #data type #mining

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

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

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

KDD-2009-NijssenGR #approach #constraints #correlation #mining #programming

Correlated itemset mining in ROC space: a constraint programming approach (SN, TG, LDR), pp. 647–656.

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.

ICPR-2008-ShidaraKN #classification #consistency

Classification by bagged consistent itemset rules (YS, MK, AN), pp. 1–4.

KDD-2008-JinAXR #effectiveness #performance #summary

Effective and efficient itemset pattern summarization: regression-based approaches (RJ, MAA, YX, NR), pp. 399–407.

KDD-2008-RaedtGN #constraints #mining #programming

Constraint programming for itemset mining (LDR, TG, SN), pp. 204–212.

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

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

SAC-2008-YamamotoOR #information management #interactive

Including the user in the knowledge discovery loop: interactive itemset-driven rule extraction (CHY, MCFO, SOR), pp. 1212–1217.

VLDB-2007-LiL #mining #multi #optimisation

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

KDD-2007-NijssenF #mining

Mining optimal decision trees from itemset lattices (SN, ÉF), pp. 530–539.

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-ShidaraNK #classification #consistency #named

CCIC: Consistent Common Itemsets Classifier (YS, AN, MK), pp. 490–498.

SAC-2007-CaldersGM #mining

Mining itemsets in the presence of missing values (TC, BG, MM), pp. 404–408.

SAC-2007-LianCY #database #maintenance #scalability

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

SAC-2007-LinL #mining #privacy #transaction

Privacy preserving itemset mining through fake transactions (JLL, JYCL), pp. 375–379.

DocEng-2006-TesarSJP #comparison #documentation

Extending the single words-based document model: a comparison of bigrams and 2-itemsets (RT, VS, KJ, MP), pp. 138–146.

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

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

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

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

KDD-2006-KnobbeH #performance

Maximally informative k-itemsets and their efficient discovery (AJK, EKYH), pp. 237–244.

KDD-2006-WangP #modelling #probability #using

Summarizing itemset patterns using probabilistic models (CW, SP), pp. 730–735.

SAC-2006-DextersPG #algorithm #analysis #probability

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

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.

KDD-2005-YanCHX #approach

Summarizing itemset patterns: a profile-based approach (XY, HC, JH, DX), pp. 314–323.

MLDM-2005-LaurSNP #data type #statistics

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

SAC-2005-ShangS #database #mining #relational

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

SAC-2005-SongR #transaction

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

PODS-2004-Calders #complexity #satisfiability

Computational Complexity of Itemset Frequency Satisfiability (TC), pp. 143–154.

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.

KDD-2004-JaroszewiczS #network #using

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

KDD-2004-SeppanenM

Dense itemsets (JKS, HM), pp. 683–688.

KDD-2004-Yang #complexity #mining

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

SEKE-2004-LiLCWL #using

Extracting Minimal Non-Redundant Implication Rules by Using Quantized Closed Itemset Lattice (YL, ZL, WC, QW, WL), pp. 402–405.

SAC-2004-Goethals #memory management #mining

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

SAC-2004-MassonRB #database #induction #optimisation #query #set #towards

Optimizing subset queries: a step towards SQL-based inductive databases for itemsets (CM, CR, JFB), pp. 535–539.

SAC-2004-Savinov #dependence #mining

Mining dependence rules by finding largest itemset support quota (AAS), pp. 525–529.

PODS-2003-RameshMZ #data mining #mining #theory and practice

Feasible itemset distributions in data mining: theory and application (GR, WM, MJZ), pp. 284–295.

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.

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

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

KDD-2003-WangHP #mining

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

SAC-2003-JiaPP #constraints #mining

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

KDD-2002-BucilaGKW #algorithm #constraints #named

DualMiner: a dual-pruning algorithm for itemsets with constraints (CB, JG, DK, WMW), pp. 42–51.

KDD-2001-YangFB #performance

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

PODS-2000-MorishitaS #metric #statistics #traversal

Traversing Itemset Lattice with Statistical Metric Pruning (SM, JS), pp. 226–236.

VLDB-2000-WangHH #constraints #mining #using

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

CL-2000-BastidePTSL #mining #using

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

KDD-1999-AyanTA #algorithm #performance #scalability

An Efficient Algorithm to Update Large Itemsets with Early Pruning (NFA, AUT, MEA), pp. 287–291.

KDD-1999-MeretakisW #classification #naive bayes #using

Extending Naïve Bayes Classifiers Using Long Itemsets (DM, BW), pp. 165–174.

PODS-1998-AggarwalY #framework #generative

A New Framework For Itemset Generation (CCA, PSY), pp. 18–24.

CIKM-1998-Tang #incremental #performance #using

Using Incremental Pruning to Increase the Efficiency of Dynamic Itemset Counting for Association Rules (JT), pp. 273–280.

SIGMOD-1997-BrinMUT

Dynamic Itemset Counting and Implication Rules for Market Basket Data (SB, RM, JDU, ST), pp. 255–264.