| 本书是综合运用数据挖掘、数据分析、信息理论通讯机器学习技术的里程碑。
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| Lan H.Witten，新西兰怀卡托大学计算机科学系教授。他是ACM和新西兰皇家学会的成员，并参加了英国、美国、加拿大和新西兰的专业计算、信息检索、工程等协会。他著有多部著作，是多家技术杂志的作者，发表过大量论文。
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| Foreword Preface 1 What's it all about? 1.1 Data mining and machine learning 1.2 Simple examples:The weather problem and others 1.3 Fielded application 1.4 Machine learning and statistics 1.5 Generalization as search 1.6 Data mining and ethics 1.7 Further reading 2 Input:Concepts,instances,attributes 2.1 What's a concept? 2.2 What's in an example? 2.3 What's in an attribute? 2.4 Preparing the input 2.5 Further reading 3 Output:Knowledge representation 3.1 Decision tables 3.2 Decision trees 3.3 Classification rules 3.4 Association rules 3.5 Rules with exceptions 3.6 Rules involving relations 3.7 Trees for numeric prediction 3.8 Instance-based representation 3.9 Clusters 3.10 Further reading 4 Algorithms:The basic methods 4.1 Infereing rudimentary rules 4.2 Statistical modeling 4.3 Divide and conuquer:Constructing decision trees 4.4 Covering algorithms:Construsting rules 4.5 Mining association rules 4.6 Linear models 4.7 Instance-based learning 4.8 Further reading 5 Credibility:Evaluation what's been learnde 5.1 Training and testing 5.2 predicting per formance 5.3 Cross-vaidation 5.4 Other estimates 5.5 Comparing data mining schems 5.6 Predicting Probabilities 5.7 Counting the cost 5.8 Evaluating numer ic prediction 5.9 The minimum description length principle 5.10 Applying MDL to clustering 5.11 Further reading 6 Implemententation:Real machine learning schemes 6.1 Decision tress 6.2 Classification rules 6.3 Extending linear classification:Support vector machines 6.4 Instance-based learning 6.5 Numeric prediction 6.6 Clustering 7 Moving on:Engineering the input and output 7.1 Attribute selection 7.2 Discretizing numeric attributes 7.3 Automtic data cleansing 7.4 Combining multiple models 7.5 Further reading 8 Nuts and bolts:Machine learning algorithms in Java 8.1 Getting started 8.2 Javadoc and the class library 8.3 Processing dataset using the machine learning programs 8.4 Embedded machine learning 8.5 Writing new learning schemes 9 Looking forward 9.1 learning from massive datasets 9.2 Visualizing machine learning 9.3 Incorporation domain knowlgdge 9.4 Text mining 9.5 Mining the World Wide Web 9.6 Further reading References Index About the authors |
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