Discovering Knowledge in Data: An Introduction to Data Mining

241 Pages·2004·5.91 MB·English

by  Daniel T. Larose

WY045-FM September24,2004 10:2
DISCOVERING
KNOWLEDGE IN DATA
An Introduction to Data Mining
DANIEL T. LAROSE
DirectorofDataMining
CentralConnecticutStateUniversity
AJOHNWILEY&SONS,INC.,PUBLICATION
iii
WY045-FM September24,2004 10:2
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WY045-FM September24,2004 10:2
DISCOVERING
KNOWLEDGE IN DATA
i
WY045-FM September24,2004 10:2
ii
WY045-FM September24,2004 10:2
DISCOVERING
KNOWLEDGE IN DATA
An Introduction to Data Mining
DANIEL T. LAROSE
DirectorofDataMining
CentralConnecticutStateUniversity
AJOHNWILEY&SONS,INC.,PUBLICATION
iii
WY045-FM September24,2004 10:2
Copyright©2005byJohnWiley&Sons,Inc.Allrightsreserved.
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LibraryofCongressCataloging-in-PublicationData:
Larose,DanielT.
Discoveringknowledgeindata:anintroductiontodatamining/DanielT.Larose
p. cm.
Includesbibliographicalreferencesandindex.
ISBN0-471-66657-2(cloth)
1.Datamining. I.Title.
QA76.9.D343L38 2005
006.3(cid:1)12—dc22 2004003680
PrintedintheUnitedStatesofAmerica
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Dedication
Tomyparents,
Andtheirparents,
Andsoon...
Formychildren,
Andtheirchildren,
Andsoon...
2004 Chantal Larose
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CONTENTS
PREFACE xi
1 INTRODUCTIONTODATAMINING 1
WhatIsDataMining? 2
WhyDataMining? 4
NeedforHumanDirectionofDataMining 4
Cross-IndustryStandardProcess:CRISP–DM 5
CaseStudy1:AnalyzingAutomobileWarrantyClaims:Exampleofthe
CRISP–DMIndustryStandardProcessinAction 8
FallaciesofDataMining 10
WhatTasksCanDataMiningAccomplish? 11
Description 11
Estimation 12
Prediction 13
Classification 14
Clustering 16
Association 17
CaseStudy2:PredictingAbnormalStockMarketReturnsUsing
NeuralNetworks 18
CaseStudy3:MiningAssociationRulesfromLegalDatabases 19
CaseStudy4:PredictingCorporateBankruptciesUsingDecisionTrees 21
CaseStudy5:ProfilingtheTourismMarketUsingk-MeansClusteringAnalysis 23
References 24
Exercises 25
2 DATAPREPROCESSING 27
WhyDoWeNeedtoPreprocesstheData? 27
DataCleaning 28
HandlingMissingData 30
IdentifyingMisclassifications 33
GraphicalMethodsforIdentifyingOutliers 34
DataTransformation 35
Min–MaxNormalization 36
Z-ScoreStandardization 37
NumericalMethodsforIdentifyingOutliers 38
References 39
Exercises 39
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