- Cover image
- Title page
- Table of Contents
- Copyright
- List of Figures
- List of Tables
- Preface
- Part I: Introduction to data mining
- Chapter 1. What’s it all about?
- Chapter 2. Input: Concepts, instances, attributes
- Chapter 3. Output: Knowledge representation
- Chapter 4. Algorithms: The basic methods
- Abstracts
- 4.1 Inferring Rudimentary Rules
- 4.2 Simple Probabilistic 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 Multi-instance Learning
- 4.10 Further Reading and Bibliographic Notes
- 4.11 Weka Implementations
- Chapter 5. Credibility: Evaluating what’s been learned
- Abstract
- 5.1 Training and Testing
- 5.2 Predicting Performance
- 5.3 Cross-Validation
- 5.4 Other Estimates
- 5.5 Hyperparameter Selection
- 5.6 Comparing Data Mining Schemes
- 5.7 Predicting Probabilities
- 5.8 Counting the Cost
- 5.9 Evaluating Numeric Prediction
- 5.10 The MDL Principle
- 5.11 Applying the MDL Principle to Clustering
- 5.12 Using a Validation Set for Model Selection
- 5.13 Further Reading and Bibliographic Notes
- Part II: More advanced machine learning schemes
- 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
- Abstract
- 9.1 Foundations
- 9.2 Bayesian Networks
- 9.3 Clustering and Probability Density Estimation
- 9.4 Hidden Variable Models
- 9.5 Bayesian Estimation and Prediction
- 9.6 Graphical Models and Factor Graphs
- 9.7 Conditional Probability Models
- 9.8 Sequential and Temporal Models
- 9.9 Further Reading and Bibliographic Notes
- 9.10 Weka Implementations
- Chapter 10. Deep learning
- Abstract
- 10.1 Deep Feedforward Networks
- 10.2 Training and Evaluating Deep Networks
- 10.3 Convolutional Neural Networks
- 10.4 Autoencoders
- 10.5 Stochastic Deep Networks
- 10.6 Recurrent Neural Networks
- 10.7 Further Reading and Bibliographic Notes
- 10.8 Deep Learning Software and Network Implementations
- 10.9 WEKA Implementations
- Chapter 11. Beyond supervised and unsupervised learning
- Chapter 12. Ensemble learning
- Chapter 13. Moving on: applications and beyond
- Abstract
- 13.1 Applying Machine Learning
- 13.2 Learning From Massive Datasets
- 13.3 Data Stream Learning
- 13.4 Incorporating Domain Knowledge
- 13.5 Text Mining
- 13.6 Web Mining
- 13.7 Images and Speech
- 13.8 Adversarial Situations
- 13.9 Ubiquitous Data Mining
- 13.10 Further Reading and Bibliographic Notes
- 13.11 WEKA Implementations
- Appendix A. Theoretical foundations
- Appendix B. The WEKA workbench
- References
- Index
Data Mining
Practical Machine Learning Tools and Techniques
Fourth Edition
Ian H. Witten
University of Waikato, Hamilton, New Zealand
Eibe Frank
University of Waikato, Hamilton, New Zealand
Mark A. Hall
University of Waikato, Hamilton, New Zealand
Christopher J. Pal
Polytechnique Montréal, Montreal, Quebec, Canada
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