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• Cover Image
• Content
• Title
• The Morgan Kaufmann Series in Data Management Systems
• Copyright
• Foreword
• List of Figures
• List of Tables
• Preface
• Updated and revised content
• Acknowledgments
• PART I: Machine learning tools and techniques
• Chapter 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
• Chapter 2. Input: Concepts, Instances, and 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
• Chapter 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
• Chapter 4. Algorithms: The Basic Methods
• 4.1 Inferring rudimentary rules
• 4.2 Statistical modeling
• 4.3 Divide-and-conquer: Constructing decision trees
• 4.5 Mining association rules
• 4.6 Linear models
• 4.7 Instance-based learning
• 4.8 Clustering
• 4.9 Further reading
• Chapter 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 methods
• 5.6 Predicting probabilities
• 5.7 Counting the cost
• 5.8 Evaluating numeric prediction
• 5.9 The minimum description length principle
• 5.10 Applying the MDL principle to clustering
• 5.11 Further reading
• Chapter 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
• Chapter 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
• Chapter 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
• Chapter 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?
• Chapter 10. The Explorer
• 10.1 Getting started
• 10.2 Exploring the Explorer
• 10.3 Filtering algorithms
• 10.4 Learning algorithms
• 10.5 Metalearning algorithms
• 10.6 Clustering algorithms
• 10.7 Association-rule learners
• 10.8 Attribute selection
• Chapter 11. The Knowledge Flow Interface
• 11.1 Getting started
• 11.2 The Knowledge Flow components
• 11.3 Configuring and connecting the components
• 11.4 Incremental learning
• Chapter 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
• Chapter 13. The Command-line Interface
• 13.1 Getting started
• 13.2 The structure of Weka
• 13.3 Command-line options
• Chapter 14. Embedded Machine Learning
• 14.1 A simple data mining application
• 14.2 Going through the code
• Chapter 15. Writing New Learning Schemes
• 15.1 An example classifier
• 15.2 Conventions for implementing classifiers
• Index
• About the Authors