Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today’s techniques coupled with the methods at the leading edge of contemporary research
1. Part I: Introduction To Data Mining - p. xl 2. Chapter 1. What’s it all about? - p. 3 3. Chapter 2. Input: concepts, instances, attributes - p. 43 4. chapter 3. Output: knowledge representation - p. 67 5. Chapter 4. Algorithms: the basic methods - p. 91 6. Chapter 5. Credibility: evaluating what’s been learned - p. 165 7. Chapter 6. Preparation: data preprocessing and exploratory data analysis - p. 209 8. Chapter 7. Ethics: what are the impacts of what’s been learned? - p. 261 9. Part II. More advanced machine learning schemes - p. 317 10. Chapter 8. Ensemble learning - p. 323 11. chapter 9. Extending instance-based and linear models - p. 347 12. Chapter 10. Deep learning: fundamentals - p. 391 13. Chapter 11. Advanced deep learning methods - p. 433 14. Chapter 12. Beyond supervised and unsupervised learning - p. 473 15. Chapter 13. Probabilistic methods: fundamentals - p. 485 16. Chapter 14. Advanced probabilistic methods - p. 516 17. Abstract - p. 516 18. Chapter Outline - p. 516 19. 14.1 Hidden Variable Models - p. 516 20. 14.2 Bayesian Estimation and Prediction - p. 520 21. 14.3 Graphical Models and Factor Graphs - p. 523 22. 14.4 Conditional Probability Models - p. 545 23. 14.5 Sequential and Temporal Models - p. 557 24. 14.6 Further Reading and Bibliographic Notes - p. 565 25. 14.7 WEKA Implementations - p. 569 26. 14.8 Exercises - p. 569 27. Chapter 15. Moving on: applications and their consequences - p. 571 28. Appendix A. : Theoretical foundations - p. 617 29. Appendix B. : The WEKA workbench - p. 637 30. Appendix C. : Implementation details of trees and rules - p. 655 31. Appendix D. : Technical details of deep learning - p. 687 32. References - p. 703 33. Index - p. 737
Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.
Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today’s techniques coupled with the methods at the leading edge of contemporary research
1. Part I: Introduction To Data Mining - p. xl 2. Chapter 1. What’s it all about? - p. 3 3. Chapter 2. Input: concepts, instances, attributes - p. 43 4. chapter 3. Output: knowledge representation - p. 67 5. Chapter 4. Algorithms: the basic methods - p. 91 6. Chapter 5. Credibility: evaluating what’s been learned - p. 165 7. Chapter 6. Preparation: data preprocessing and exploratory data analysis - p. 209 8. Chapter 7. Ethics: what are the impacts of what’s been learned? - p. 261 9. Part II. More advanced machine learning schemes - p. 317 10. Chapter 8. Ensemble learning - p. 323 11. chapter 9. Extending instance-based and linear models - p. 347 12. Chapter 10. Deep learning: fundamentals - p. 391 13. Chapter 11. Advanced deep learning methods - p. 433 14. Chapter 12. Beyond supervised and unsupervised learning - p. 473 15. Chapter 13. Probabilistic methods: fundamentals - p. 485 16. Chapter 14. Advanced probabilistic methods - p. 516 17. Abstract - p. 516 18. Chapter Outline - p. 516 19. 14.1 Hidden Variable Models - p. 516 20. 14.2 Bayesian Estimation and Prediction - p. 520 21. 14.3 Graphical Models and Factor Graphs - p. 523 22. 14.4 Conditional Probability Models - p. 545 23. 14.5 Sequential and Temporal Models - p. 557 24. 14.6 Further Reading and Bibliographic Notes - p. 565 25. 14.7 WEKA Implementations - p. 569 26. 14.8 Exercises - p. 569 27. Chapter 15. Moving on: applications and their consequences - p. 571 28. Appendix A. : Theoretical foundations - p. 617 29. Appendix B. : The WEKA workbench - p. 637 30. Appendix C. : Implementation details of trees and rules - p. 655 31. Appendix D. : Technical details of deep learning - p. 687 32. References - p. 703 33. Index - p. 737
