Frequent Pattern Mining

480 Pages·2014·11.111 MB·English

by  Charu C. Aggarwal, Jiawei Han (eds.)

Frequent Pattern Mining
Charu C. Aggarwal • Jiawei Han
Editors
Frequent Pattern Mining
2123
Editors
CharuC.Aggarwal JiaweiHan
IBMT.J.WatsonResearchCenter UniversityofIllinoisatUrbana-Champaign
YorktownHeights Urbana
NewYork Illinois
USA USA
ISBN978-3-319-07820-5 ISBN978-3-319-07821-2(eBook)
DOI10.1007/978-3-319-07821-2
SpringerChamHeidelbergNewYorkDordrechtLondon
LibraryofCongressControlNumber:2014944536
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Preface
Thefieldofdatamininghasfourmain“super-problems”correspondingtoclustering,
classification,outlieranalysis,andfrequentpatternmining.Comparedtotheother
threeproblems,thefrequentpatternminingmodelforformulatedrelativelyrecently.
In spite of its shorter history, frequent pattern mining is considered the marquee
problemofdatamining.Thereasonforthisisthatinterestinthedataminingfield
increasedrapidlysoonaftertheseminalpaperonassociationruleminingbyAgrawal,
Imielinski, andSwami.Theearlierdataminingconferenceswereoftendominated
byalargenumberoffrequentpatternminingpapers.Thisisoneofthereasonsthat
frequentpatternmininghasaveryspecialplaceinthedataminingcommunity.At
thispoint,thefieldoffrequentpatternminingisconsideredamatureone.
While the field has reached a relative level of maturity, very few books cover
different aspects of frequent pattern mining. Most of the existing books are either
too generic or do not cover frequent pattern mining in an exhaustive way.A need
existsforanexhaustivebookonthetopicthatcancoverthedifferentnuancesinan
exhaustiveway.
Thisbookprovidescomprehensivesurveysinthefieldoffrequentpatternmining.
Eachchapterisdesignedasasurveythatcoversthekeyaspectsofthefieldoffrequent
patternmining.Thechaptersaretypicallyofthefollowingtypes:
(cid:129) Algorithms: In these cases, the key algorithms for frequent pattern mining are
explored.Theseincludejoin-basedmethodssuchasApriori,andpattern-growth
methods.
(cid:129) Variations: Many variations of frequent pattern mining such as interesting pat-
terns, negativepatterns, constrainedpatternmining, orcompressedpatternsare
exploredinthesechapters.
(cid:129) Scalability:Thelargesizesofdatainrecentyearshasledtotheneedforbigdata
andstreamingframeworksforfrequentpatternmining.Frequentpatternmining
algorithmsneedtobemodifiedtoworkwiththeseadvancedscenarios.
(cid:129) DataTypes:Differentdatatypesleadtodifferentchallengesforfrequentpattern
miningalgorithms. Frequentpatternminingalgorithmsneedtobeabletowork
withcomplexdatatypes,suchastemporalorgraphdata.
v
vi Preface
(cid:129) Applications:Inthesechapters,differentapplicationsoffrequentpatternmining
areexplored.Theseincludestheapplicationoffrequentpatternminingmethods
toproblemssuchasclusteringandclassification.Othermorecomplexalgorithms
arealsoexplored.
This book is, therefore, intended to provide an overview of the field of frequent
patternmining,asitcurrentlystands.Itishopedthatthebookwillserveasauseful
guideforstudents,researchers,andpractitioners.
Contents
1 AnIntroductiontoFrequentPatternMining ..................... 1
CharuC.Aggarwal
1 Introduction............................................... 1
2 FrequentPatternMiningAlgorithms .......................... 3
2.1 Frequent Pattern Mining with the Traditional Support
Framework .......................................... 4
2.2 InterestingandNegativeFrequentPatterns ................ 6
2.3 ConstrainedFrequentPatternMining..................... 7
2.4 CompressedRepresentationsofFrequentPatterns .......... 7
3 ScalabilityIssuesinFrequentPatternMining................... 8
3.1 FrequentPatternMininginDataStreams ................. 8
3.2 FrequentPatternMiningwithBigData ................... 9
4 FrequentPatternMiningwithAdvancedDataTypes ............. 9
4.1 SequentialPatternMining .............................. 10
4.2 SpatiotemporalPatternMining .......................... 10
4.3 FrequentPatternsinGraphsandStructuredData ........... 11
4.4 FrequentPatternMiningwithUncertainData.............. 11
5 PrivacyIssues ............................................. 12
6 ApplicationsofFrequentPatternMining....................... 13
6.1 ApplicationstoMajorDataMiningProblems.............. 13
6.2 GenericApplications .................................. 13
7 ConclusionsandSummary .................................. 14
References.................................................... 14
2 FrequentPatternMiningAlgorithms:ASurvey .................. 19
CharuC.Aggarwal,MansurulA.BhuiyanandMohammadAlHasan
1 Introduction............................................... 19
1.1 Definitions........................................... 22
2 Join-BasedAlgorithms...................................... 23
2.1 AprioriMethod ....................................... 24
2.2 DHPAlgorithm....................................... 27
2.3 SpecialTricksfor2-ItemsetCounting .................... 28
vii
viii Contents
2.4 PruningbySupportLowerBounding..................... 28
2.5 HypercubeDecomposition.............................. 29
3 Tree-BasedAlgorithms ..................................... 29
3.1 AISAlgorithm........................................ 31
3.2 TreeProjectionAlgorithms.............................. 32
3.3 VerticalMiningAlgorithms ............................. 36
4 RecursiveSuffix-BasedGrowth .............................. 39
4.1 TheFP-GrowthApproach .............................. 41
4.2 Variations............................................ 45
5 MaximalandClosedFrequentItemsets........................ 47
5.1 Definitions........................................... 47
5.2 FrequentMaximalItemsetMiningAlgorithms ............. 48
5.3 FrequentClosedItemsetMiningAlgorithms............... 55
6 OtherOptimizationsandVariations ........................... 57
6.1 RowEnumerationMethods ............................. 57
6.2 OtherExplorationStrategies ............................ 58
7 ReducingtheNumberofPasses .............................. 58
7.1 CombiningPasses..................................... 58
7.2 SamplingTricks ...................................... 59
7.3 OnlineAssociationRuleMining......................... 60
8 ConclusionsandSummary .................................. 61
References.................................................... 61
3 Pattern-GrowthMethods ...................................... 65
JiaweiHanandJianPei
1 Introduction............................................... 66
2 FP-Growth:PatternGrowthforMiningFrequentItemsets ........ 68
3 PushingMoreConstraintsinPattern-GrowthMining ............ 72
4 PrefixSpan:MiningSequentialPatternsbyPatternGrowth ....... 74
5 Further Development of Pattern Growth-Based Pattern Mining
Methodology.............................................. 77
6 Conclusions............................................... 78
References.................................................... 79
4 MiningLongPatterns ......................................... 83
FeidaZhu
1 Introduction............................................... 83
2 Preliminaries.............................................. 84
3 APatternLatticeModel..................................... 86
4 PatternEnumerationApproach............................... 87
4.1 Breadth-FirstApproach ................................ 87
4.2 Depth-FirstApproach.................................. 88
5 RowEnumerationApproach................................. 89
6 PatternMergeApproach .................................... 92
6.1 Piece-wisePatternMerge............................... 93
Contents ix
6.2 Fusion-stylePatternMerge ............................. 98
7 PatternTraversalApproach .................................. 101
8 Conclusion ............................................... 102
References.................................................... 103
5 InterestingPatterns ........................................... 105
JillesVreekenandNikolajTatti
1 Introduction............................................... 106
2 AbsoluteMeasures......................................... 107
2.1 FrequentItemsets ..................................... 107
2.2 Tiles ................................................ 112
2.3 LowEntropySets ..................................... 114
3 AdvancedMethods......................................... 114
4 StaticBackgroundModels .................................. 115
4.1 IndependenceModel .................................. 116
4.2 BeyondIndependence ................................. 119
4.3 MaximumEntropyModels ............................. 120
4.4 RandomizationApproaches............................. 123
5 DynamicBackgroundModels................................ 124
5.1 TheGeneralIdea...................................... 125
5.2 MaximumEntropyModels ............................. 125
5.3 Tile-basedTechniques ................................. 126
5.4 SwapRandomization .................................. 128
6 PatternSets ............................................... 128
6.1 Itemsets ............................................. 129
6.2 Tiles ................................................ 130
6.3 SwapRandomization .................................. 130
7 Conclusions............................................... 131
References.................................................... 132
6 NegativeAssociationRules ..................................... 135
LuizaAntonie,JundongLiandOsmarZaiane
1 Introduction............................................... 135
2 NegativePatternsandNegativeAssociationRules............... 136
3 CurrentApproaches ........................................ 138
4 AssociativeClassificationandNegativeAssociationRules........ 143
5 Conclusions............................................... 143
References.................................................... 144
7 Constraint-BasedPatternMining ............................... 147
SiegfriedNijssenandAlbrechtZimmermann
1 Introduction............................................... 147
2 ProblemDefinition......................................... 148
2.1 Constraints........................................... 149
3 Level-WiseAlgorithm ...................................... 152
x Contents
3.1 GenericAlgorithm .................................... 153
4 Depth-FirstAlgorithm ...................................... 154
4.1 BasicAlgorithm ...................................... 154
4.2 Constraint-basedItemsetMining ........................ 155
4.3 GenericFrameworks................................... 158
4.4 ImplementationConsiderations.......................... 159
5 Languages................................................ 159
6 Conclusions............................................... 162
References.................................................... 162
8 MiningandUsingSetsofPatternsthroughCompression .......... 165
MatthijsvanLeeuwenandJillesVreeken
1 Introduction............................................... 165
2 Foundations............................................... 167
2.1 KolmogorovComplexity ............................... 168
2.2 MDL................................................ 169
2.3 MDLinDataMining .................................. 171
3 Compression-basedPatternModels ........................... 171
3.1 PatternModelsforMDL ............................... 172
3.2 CodeTables.......................................... 173
3.3 InstancesofCompression-basedModels .................. 179
4 AlgorithmicApproaches .................................... 181
4.1 CandidateSetFiltering................................. 181
4.2 DirectMiningofPatternsthatCompress.................. 184
5 MDLforDataMining ...................................... 185
5.1 Classification......................................... 186
5.2 ADissimilarityMeasureforDatasets..................... 188
5.3 IdentifyingandCharacterizingComponents ............... 189
5.4 OtherDataMiningTasks............................... 191
5.5 TheAdvantageofPattern-basedModels .................. 192
6 ChallengesAhead.......................................... 193
6.1 TowardMiningStructuredData ......................... 193
6.2 Generalization........................................ 194
6.3 Task-and/orUser-specificUsefulness .................... 194
7 Conclusions............................................... 195
References.................................................... 196
9 FrequentPatternMininginDataStreams ....................... 199
VictorE.Lee,RuomingJinandGaganAgrawal
1 Introduction............................................... 200
2 Preliminaries.............................................. 201
2.1 FrequentPatternMining:Definition...................... 201
2.2 DataWindows........................................ 202
2.3 FrequentItemMining.................................. 203
3 FrequentItemsetMiningAlgorithms .......................... 204