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
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15. Writing new learning schemes
References
Index