Exploiting the power of group differences: using patterns to solve data analysis problems

147 Pages·2019·0.982 MB·English

by  Dong, Guozhu

Exploiting the Power of
Group Differences
Using Patterns to Solve Data Analysis Problems
Synthesis Lectures on Data
Mining and Knowledge
Discovery
Editors
JiaweiHan,UniversityofIllinoisatUrbana-Champaign
LiseGetoor,UniversityofCalifornia,SantaCruz
WeiWang,UniversityofCalifornia,LosAngeles
JohannesGehrke,CornellUniversity
RobertGrossman,UniversityofChicago
SynthesisLecturesonDataMiningandKnowledgeDiscoveryiseditedbyJiaweiHan,Lise
Getoor,WeiWang,JohannesGehrke,andRobertGrossman.Theseriespublishes50-to150-page
publicationsontopicspertainingtodatamining,webmining,textmining,andknowledge
discovery,includingtutorialsandcasestudies.Potentialtopicsinclude:dataminingalgorithms,
innovativedataminingapplications,dataminingsystems,miningtext,webandsemi-structured
data,highperformanceandparallel/distributeddatamining,dataminingstandards,datamining
andknowledgediscoveryframeworkandprocess,dataminingfoundations,miningdatastreams
andsensordata,miningmulti-mediadata,miningsocialnetworksandgraphdata,miningspatial
andtemporaldata,pre-processingandpost-processingindatamining,robustandscalable
statisticalmethods,security,privacy,andadversarialdatamining,visualdatamining,visual
analytics,anddatavisualization.
ExploitingthePowerofGroupDifferences:UsingPatternstoSolveDataAnalysis
Problems
GuozhuDong
2019
MiningStructuresofFactualKnowledgefromText
XiangRenandJiaweiHan
2018
IndividualandCollectiveGraphMining:Principles,Algorithms,andApplications
DanaiKoutraandChristosFaloutsos
2017
PhraseMiningfromMassiveTextandItsApplications
JialuLiu,JingboShang,andJiaweiHan
2017
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ExploratoryCausalAnalysiswithTimeSeriesData
JamesM.McCracken
2016
MiningHumanMobilityinLocation-BasedSocialNetworks
HuijiGaoandHuanLiu
2015
MiningLatentEntityStructures
ChiWangandJiaweiHan
2015
ProbabilisticApproachestoRecommendations
NicolaBarbieri,GiuseppeManco,andEttoreRitacco
2014
OutlierDetectionforTemporalData
ManishGupta,JingGao,CharuAggarwal,andJiaweiHan
2014
ProvenanceDatainSocialMedia
GeoffreyBarbier,ZhuoFeng,PritamGundecha,andHuanLiu
2013
GraphMining:Laws,Tools,andCaseStudies
D.ChakrabartiandC.Faloutsos
2012
MiningHeterogeneousInformationNetworks:PrinciplesandMethodologies
YizhouSunandJiaweiHan
2012
PrivacyinSocialNetworks
ElenaZheleva,EvimariaTerzi,andLiseGetoor
2012
CommunityDetectionandMininginSocialMedia
LeiTangandHuanLiu
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EnsembleMethodsinDataMining:ImprovingAccuracyThroughCombining
Predictions
GiovanniSeniandJohnF.Elder
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ModelingandDataMininginBlogosphere
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Copyright©2019byMorgan&Claypool
Allrightsreserved.Nopartofthispublicationmaybereproduced,storedinaretrievalsystem,ortransmittedin
anyformorbyanymeans—electronic,mechanical,photocopy,recording,oranyotherexceptforbriefquotations
inprintedreviews,withoutthepriorpermissionofthepublisher.
ExploitingthePowerofGroupDifferences:UsingPatternstoSolveDataAnalysisProblems
GuozhuDong
www.morganclaypool.com
ISBN:9781681735023 paperback
ISBN:9781681735030 ebook
ISBN:9781681735047 hardcover
DOI10.2200/S00897ED1V01Y201901DMK016
APublicationintheMorgan&ClaypoolPublishersseries
SYNTHESISLECTURESONDATAMININGANDKNOWLEDGEDISCOVERY
Lecture#16
SeriesEditors:JiaweiHan,UniversityofIllinoisatUrbana-Champaign
LiseGetoor,UniversityofCalifornia,SantaCruz
WeiWang,UniversityofCalifornia,LosAngeles
JohannesGehrke,CornellUniversity
RobertGrossman,UniversityofChicago
SeriesISSN
Print2151-0067 Electronic2151-0075
Exploiting the Power of
Group Differences
Using Patterns to Solve Data Analysis Problems
Guozhu Dong
WrightStateUniversity
SYNTHESISLECTURESONDATAMININGANDKNOWLEDGE
DISCOVERY#16
M
&C Morgan &cLaypool publishers
ABSTRACT
This book presents pattern-based problem-solving methods for a variety of machine learning
and data analysis problems. The methods are all based on techniques that exploit the power of
groupdifferences.Theymakeuseofgroupdifferencesrepresentedusingemergingpatterns(aka
contrastpatterns),whicharepatternsthatmatchsignificantlydifferentnumbersofinstancesin
different data groups. A large number of applications outside of the computing discipline are
alsoincluded.
Emerging patterns (EPs) are useful in many ways. EPs can be used as features, as sim-
ple classifiers, as subpopulation signatures/characterizations, and as triggering conditions for
alerts. EPs can be used in gene ranking for complex diseases since they capture multi-factor
interactions.ThelengthofEPscanbeusedtodetectanomalies,outliers,andnovelties.Emerg-
ing/contrast pattern-based methods for clustering analysis and outlier detection do not need
distancemetrics,avoidingpitfallsofthelatterinexploratoryanalysisofhighdimensionaldata.
EP-basedclassifierscanachievegoodaccuracyevenwhenthetrainingdatasetsaretiny,making
themusefulforexploratorycompoundselectionindrugdesign.EPscanserveasopportunities
inopportunity-focusedboostingandareusefulforconstructingpowerfulconditionalensembles.
EP-basedmethodsoftenproduceinterpretablemodelsandresults.Ingeneral,EPsareusefulfor
classification,clustering,outlierdetection,generankingforcomplexdiseases,predictionmodel
analysisandimprovement,andsoon.
EPs are useful for many tasks because they represent group differences, which have ex-
traordinarypower.Moreover,EPsrepresentmulti-factorinteractions,whoseeffectivehandling
isofvitalimportanceandisamajorchallengeinmanydisciplines.
Based on the results presented in this book, one can clearly say that patterns are useful,
especiallywhentheyarelinkedtoissuesofinterest.
Webelievethatmanyeffectivewaystoexploitgroupdifferences’powerstillremaintobe
discovered.Hopefullythisbookwillinspirereaderstodiscoversuchnewways,besidesshowing
themexistingways,tosolvevariouschallengingproblems.
KEYWORDS
data mining, machine learning, data analytic, classification, regression, cluster-
ing, anomaly detection, outlier detection, intrusion detection, compound selec-
tion,complexdiseaseanalysis,extremeinstanceselection,factorranking,prediction
model analysis, group difference analysis, feature, multifactor interaction, diverse
relationship,heterogeneity,boosting,ensemble,associationrule,emergingpattern,
contrastpattern,frequentpattern,distancemetric,interpretability
vii
To my wife for her love and support!
– Guozhu Dong

ix
Contents
Acknowledgments ................................................. xv
1 IntroductionandOverview ...........................................1
1.1 ImportanceofGroupDifferences .................................... 1
1.2 SummaryofChapters ............................................. 2
1.2.1 ReadingOrderoftheChapters ................................ 5
1.3 KnownUsesofGroupDifferencesviaEmergingPatterns................. 5
1.4 UniquePropertiesofEmergingPatternBasedMethods .................. 6
1.5 ScenariosWhereEmergingPatternsAreEspeciallyUseful................ 7
1.6 RelatedTopicsNotCoveredinThisBook ............................. 8
2 GeneralPreliminaries ...............................................9
2.1 Attributes,Features,andVariables ................................... 9
2.2 DataInstancesandDatasets ........................................ 9
2.3 AttributeBinningandDiscretization ................................ 10
2.4 Patterns,MatchingDatasets,Supports,andFrequentPatterns............ 12
2.5 EquivalenceClasses,ClosedPatterns,MinimalGenerators,andBorders.... 13
2.6 IllustratingExamples ............................................. 13
3 EmergingPatternsandaFlexibleMiningAlgorithm.....................15
3.1 SettingforGroupDifferenceAnalysis ............................... 15
3.2 BasicsofEmergingPatterns ....................................... 15
3.3 BorderDiff:ASimple,FlexibleEmergingPatternMiningAlgorithm ........ 18
3.4 WhatEmergingPatternsCanRepresent ............................. 20
3.5 ComparisonwithAssociationRules,Confidence,andOddsRatio......... 21
3.6 PointerstoSectionsIllustratingUsesofEmergingPatterns .............. 23
3.7 TraditionalAnalysisofGroupDifferences ............................ 24
3.8 DiscussionofRelatedIssues ....................................... 25