Preface PART I INTRODUCTION TO DATA MINING CHAPTER 1 What's it all about? 1.1 Data Mining and Machine Learning Describing Structural Patterns Machine Learning Data Mining 1.2 Simple Examples: The Weather Problem and Others The Weather Problem Contact Lenses: An Idealized Problem Irises: A Classic Numeric Dataset CPU Performance: Introducing Numeric Prediction Labor Negotiations: A More Realistic Example Soybean Classification: A Classic Machine Learning Success 1.3 Fielded Applications Web Mining Decisions Involving Judgment Screening Images Load Forecasting Diagnosis Marketing and Sales Other Applications 1.4The Data Mining Process 1.5 Machine Learning and Statistics 1.6 Generalization as Search Enumerating the Concept Space Bias 1.7 Data Mining and Ethics Reidentification Using Personal Information Wider Issues 1.8 Further Reading and Bibliographic Notes CHAPTER 2 Input: concepts, instances, attributes CHAPTER 3 Output: knowledge representation CHAPTER 4 Algorithms: the basic methods CHAPTER 5 Credibility: evaluating what's been learned PART II MORE ADVANCED MACHINE LEARNING SCHEMES CHAPTER 6 Trees and rules CHAPTER 7 Extending instance-based and linear models CHAPTER 8 Data Transformations CHAPTER 9 Probabilistic methods Chapter 10 Deep learning CHAPTER 11 Beyond supervised and unsupervised learning CHAPTER 12 Ensemble learning CHAPTER 13 Moving on : applications and beyond List of Figures List of Tables
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