Data Mining and Predictive Analytics

827 Pages·2015·6.28 MB·English

by  Daniel T. Larose

DATA MINING AND
PREDICTIVE ANALYTICS
WILEY SERIES ON METHODS AND APPLICATIONS
IN DATA MINING
Series Editor: Daniel T. Larose
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Data Mining and Predictive Analytics Daniel T. Larose and Chantal D. Larose
DATA MINING AND
PREDICTIVE ANALYTICS
Second Edition
DANIEL T. LAROSE
CHANTAL D. LAROSE
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Larose,DanielT.
Dataminingandpredictiveanalytics/DanielT.Larose,ChantalD.Larose.
pagescm.–(Wileyseriesonmethodsandapplicationsindatamining)
Includesbibliographicalreferencesandindex.
ISBN978-1-118-11619-7(cloth)
1.Datamining.2.Predictiontheory.I.Larose,ChantalD.II.Title.
QA76.9.D343L37762015
006.3′12–dc23
2014043340
Setin10/12ptTimesbyLaserwordsPrivateLimited,Chennai,India
PrintedintheUnitedStatesofAmerica
10987654321
2 2015
Tothosewhohavegonebeforeus,
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CONTENTS
PREFACE xxi
ACKNOWLEDGMENTS xxix
PARTI
DATAPREPARATION
1
CHAPTER1 ANINTRODUCTIONTODATAMININGANDPREDICTIVE
ANALYTICS 3
1.1 WhatisDataMining?WhatisPredictiveAnalytics? 3
1.2 Wanted:DataMiners 5
1.3 TheNeedforHumanDirectionofDataMining 6
1.4 TheCross-IndustryStandardProcessforDataMining:CRISP-DM 6
1.4.1 CRISP-DM:TheSixPhases 7
1.5 FallaciesofDataMining 9
1.6 WhatTasksCanDataMiningAccomplish 10
1.6.1 Description 10
1.6.2 Estimation 11
1.6.3 Prediction 12
1.6.4 Classification 12
1.6.5 Clustering 15
1.6.6 Association 16
TheRZone 17
RReferences 18
Exercises 18
CHAPTER2 DATAPREPROCESSING 20
2.1 WhydoWeNeedtoPreprocesstheData? 20
2.2 DataCleaning 21
2.3 HandlingMissingData 22
2.4 IdentifyingMisclassifications 25
2.5 GraphicalMethodsforIdentifyingOutliers 26
2.6 MeasuresofCenterandSpread 27
2.7 DataTransformation 30
2.8 Min–MaxNormalization 30
2.9 Z-ScoreStandardization 31
2.10 DecimalScaling 32
2.11 TransformationstoAchieveNormality 32
vii
viii CONTENTS
2.12 NumericalMethodsforIdentifyingOutliers 38
2.13 FlagVariables 39
2.14 TransformingCategoricalVariablesintoNumericalVariables 40
2.15 BinningNumericalVariables 41
2.16 ReclassifyingCategoricalVariables 42
2.17 AddinganIndexField 43
2.18 RemovingVariablesthatarenotUseful 43
2.19 VariablesthatShouldProbablynotbeRemoved 43
2.20 RemovalofDuplicateRecords 44
2.21 AWordAboutIDFields 45
TheRZone 45
RReference 51
Exercises 51
CHAPTER3 EXPLORATORYDATAANALYSIS 54
3.1 HypothesisTestingVersusExploratoryDataAnalysis 54
3.2 GettingtoKnowtheDataSet 54
3.3 ExploringCategoricalVariables 56
3.4 ExploringNumericVariables 64
3.5 ExploringMultivariateRelationships 69
3.6 SelectingInterestingSubsetsoftheDataforFurtherInvestigation 70
3.7 UsingEDAtoUncoverAnomalousFields 71
3.8 BinningBasedonPredictiveValue 72
3.9 DerivingNewVariables:FlagVariables 75
3.10 DerivingNewVariables:NumericalVariables 77
3.11 UsingEDAtoInvestigateCorrelatedPredictorVariables 78
3.12 SummaryofOurEDA 81
TheRZone 82
RReferences 89
Exercises 89
CHAPTER4 DIMENSION-REDUCTIONMETHODS 92
4.1 NeedforDimension-ReductioninDataMining 92
4.2 PrincipalComponentsAnalysis 93
4.3 ApplyingPCAtotheHousesDataSet 96
4.4 HowManyComponentsShouldWeExtract? 102
4.4.1 TheEigenvalueCriterion 102
4.4.2 TheProportionofVarianceExplainedCriterion 103
4.4.3 TheMinimumCommunalityCriterion 103
4.4.4 TheScreePlotCriterion 103
4.5 ProfilingthePrincipalComponents 105
4.6 Communalities 108
4.6.1 MinimumCommunalityCriterion 109
4.7 ValidationofthePrincipalComponents 110
4.8 FactorAnalysis 110
4.9 ApplyingFactorAnalysistotheAdultDataSet 111
4.10 FactorRotation 114
4.11 User-DefinedComposites 117