Table Of ContentDATA MINING
FOR BUSINESS ANALYTICS
DATA MINING
FOR BUSINESS ANALYTICS
Concepts, Techniques, and Applications in R
Galit Shmueli
Peter C. Bruce
Inbal Yahav
Nitin R. Patel
Kenneth C. Lichtendahl, Jr.
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The beginning of wisdom is this:
Get wisdom, and whatever else you get, get insight.
– Proverbs 4:7
Contents
ForewordbyGarethJames xix
ForewordbyRaviBapna xxi
PrefacetotheREdition xxiii
Acknowledgments xxvii
PART I PRELIMINARIES
CHAPTER 1 Introduction 3
1.1 WhatIsBusinessAnalytics? . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 WhatIsDataMining? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 DataMiningandRelatedTerms . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.4 BigData. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.5 DataScience . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.6 WhyAreThereSoManyDifferentMethods? . . . . . . . . . . . . . . . . . . . 8
1.7 TerminologyandNotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.8 RoadMapstoThisBook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
OrderofTopics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
CHAPTER 2 Overview of the Data Mining Process 15
2.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 CoreIdeasinDataMining . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
AssociationRulesandRecommendationSystems . . . . . . . . . . . . . . . . . 16
PredictiveAnalytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
DataReductionandDimensionReduction . . . . . . . . . . . . . . . . . . . . 17
DataExplorationandVisualization . . . . . . . . . . . . . . . . . . . . . . . . 17
SupervisedandUnsupervisedLearning . . . . . . . . . . . . . . . . . . . . . . 18
2.3 TheStepsinDataMining . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.4 PreliminarySteps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
OrganizationofDatasets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
PredictingHomeValuesintheWestRoxburyNeighborhood . . . . . . . . . . . 21
vii
viii CONTENTS
LoadingandLookingattheDatainR . . . . . . . . . . . . . . . . . . . . . . 22
SamplingfromaDatabase . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
OversamplingRareEventsinClassificationTasks . . . . . . . . . . . . . . . . . 25
PreprocessingandCleaningtheData. . . . . . . . . . . . . . . . . . . . . . . 26
2.5 PredictivePowerandOverfitting . . . . . . . . . . . . . . . . . . . . . . . . . 33
Overfitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
CreationandUseofDataPartitions . . . . . . . . . . . . . . . . . . . . . . . 35
2.6 BuildingaPredictiveModel . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
ModelingProcess . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
2.7 UsingRforDataMiningonaLocalMachine . . . . . . . . . . . . . . . . . . . 43
2.8 AutomatingDataMiningSolutions . . . . . . . . . . . . . . . . . . . . . . . . 43
DataMiningSoftware: TheStateoftheMarket(byHerbEdelstein). . . . . . . . 45
Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
PART II DATA EXPLORATION AND DIMENSION REDUCTION
CHAPTER 3 Data Visualization 55
3.1 UsesofDataVisualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
BaseRorggplot? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.2 DataExamples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Example1: BostonHousingData . . . . . . . . . . . . . . . . . . . . . . . . 57
Example2: RidershiponAmtrakTrains. . . . . . . . . . . . . . . . . . . . . . 59
3.3 BasicCharts: BarCharts,LineGraphs,andScatterPlots . . . . . . . . . . . . . 59
DistributionPlots: BoxplotsandHistograms . . . . . . . . . . . . . . . . . . . 61
Heatmaps: VisualizingCorrelationsandMissingValues . . . . . . . . . . . . . . 64
3.4 MultidimensionalVisualization . . . . . . . . . . . . . . . . . . . . . . . . . . 67
AddingVariables: Color,Size,Shape,MultiplePanels,andAnimation . . . . . . . 67
Manipulations: Rescaling,AggregationandHierarchies,Zooming,Filtering . . . . 70
Reference: TrendLinesandLabels . . . . . . . . . . . . . . . . . . . . . . . . 74
ScalinguptoLargeDatasets. . . . . . . . . . . . . . . . . . . . . . . . . . . 74
MultivariatePlot: ParallelCoordinatesPlot. . . . . . . . . . . . . . . . . . . . 75
InteractiveVisualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
3.5 SpecializedVisualizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
VisualizingNetworkedData . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
VisualizingHierarchicalData: Treemaps . . . . . . . . . . . . . . . . . . . . . 82
VisualizingGeographicalData: MapCharts . . . . . . . . . . . . . . . . . . . . 83
3.6 Summary: MajorVisualizationsandOperations,byDataMiningGoal . . . . . . . 86
Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
TimeSeriesForecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
UnsupervisedLearning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
CHAPTER 4 Dimension Reduction 91
4.1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.2 CurseofDimensionality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
