Foreword Preface Part I Machine learning tools and techniques 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 applications 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 Inferring rudimentary rules 4.2 Statistical modeling 4.3 Divide-and-conquer: constructing decision trees 4.4 Covering algorithms: constructing rules 4.5 Mining association rules 4.6 Linear models 4.7 Instance-based learning 4.8 Clustering 4.9 Further reading 5. Credibility: Evaluating what?s been learned 5.1 Training and testing 5.2 Predicting performance 5.3 Cross-validation 5.4 Other estimates 5.5 Comparing data mining schemes 5.6 Predicting probabilities 5.7 Counting the cost 5.8 Evaluating numeric prediction 5.9 The minimum description length principle 5.10 Applying MDL to clustering 5.11 Further reading 6. Implementations: Real machine learning schemes 6.1 Decision trees 6.2 Classification rules 6.3 Extending linear models 6.4 Instance-based learning 6.5 Numeric prediction 6.6 Clustering 6.7 Bayesian networks 7. Transformations: Engineering the input and output 7.1 Attribute selection 7.2 Discretizing numeric attributes 7.3 Some useful transformations 7.4 Automatic data cleansing 7.5 Combining multiple models 7.6 Using unlabeled data 7.7 Further reading 8. Moving on: Extensions and applications 8.1 Learning from massive datasets 8.2 Incorporating domain knowledge 8.3 Text and Web mining 8.4 Adversarial situations 8.5 Ubiquitous data mining 8.6 Further reading Part II: The Weka machine learning workbench 9. Introduction to Weka 9.1 What?s in Weka? 9.2 How do you use it? 9.3 What else can you do? 9.4 How do you get it? 10. The Explorer 10.1 Getting started 10.2 Exploring the Explorer 10.3 Filtering algorithms 10.4 Learning algorithms 10.5 Meta-learning algorithms 10.6 Clustering algorithms 10.7 Association-rule learners 10.8 Attribute selection 11. The Knowledge Flow interface 11.1 Getting started 11.2 Knowledge Flow components 11.3 Configuring and connecting the components 11.4 Incremental learning 12. The Experimenter 12.1 Getting started 12.2 Simple setup 12.3 Advanced setup 12.4 The Analyze panel 12.5 Distributing processing over several machines 13. The command-line interface 13.1 Getting started 13.2 The structure of Weka 13.3 Command-line options 14. Embedded machine learning …… 15. Writing new learning schemes References Index
數(shù)據(jù)倉庫與數(shù)據(jù)挖掘?qū)д?/a>
鄂公網(wǎng)安備 42010302001612號 