PrefacePART I INTRODUCTION TO DATA MININGCHAPTER 1 What's it all about?1.1 Data Mining and Machine LearningDescribing Structural PatternsMachine LearningData Mining1.2 Simple Examples: The Weather Problem and OthersThe Weather ProblemContact Lenses: An Idealized ProblemIrises: A Classic Numeric DatasetCPU Performance: Introducing Numeric PredictionLabor Negotiations: A More Realistic ExampleSoybean Classification: A Classic Machine Learning Success1.3 Fielded ApplicationsWeb MiningDecisions Involving JudgmentScreening ImagesLoad ForecastingDiagnosisMarketing and SalesOther Applications1.4The Data Mining Process1.5 Machine Learning and Statistics1.6 Generalization as SearchEnumerating the Concept SpaceBias1.7 Data Mining and EthicsReidentificationUsing Personal InformationWider Issues1.8 Further Reading and Bibliographic NotesCHAPTER 2 Input: concepts, instances, attributesCHAPTER 3 Output: knowledge representationCHAPTER 4 Algorithms: the basic methodsCHAPTER 5 Credibility: evaluating what's been learnedPART II MORE ADVANCED MACHINE LEARNING SCHEMESCHAPTER 6 Trees and rulesCHAPTER 7 Extending instance-based and linear modelsCHAPTER 8 Data TransformationsCHAPTER 9 Probabilistic methodsChapter 10 Deep learningCHAPTER 11 Beyond supervised and unsupervised learningCHAPTER 12 Ensemble learningCHAPTER 13 Moving on : applications and beyondList of FiguresList of Tables