Data Mining: Practical Machine Learning Tools and Techniques, Fourth 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 fourth 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 substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html.
It contains
1. Part I: Introduction to data mining - p. xxxii 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. Abstracts - p. 91 7. 4.1 Inferring Rudimentary Rules - p. 93 8. 4.2 Simple Probabilistic Modeling - p. 96 9. 4.3 Divide-and-Conquer: Constructing Decision Trees - p. 105 10. 4.4 Covering Algorithms: Constructing Rules - p. 113 11. 4.5 Mining Association Rules - p. 120 12. 4.6 Linear Models - p. 128 13. 4.7 Instance-Based Learning - p. 135 14. 4.8 Clustering - p. 141 15. 4.9 Multi-instance Learning - p. 156 16. 4.10 Further Reading and Bibliographic Notes - p. 158 17. 4.11 Weka Implementations - p. 160 18. Chapter 5. Credibility: Evaluating what’s been learned - p. 161 19. Part II: More advanced machine learning schemes - p. 203 20. Part II. More advanced machine learning schemes - p. 205 21. Chapter 6. Trees and rules - p. 209 22. Chapter 7. Extending instance-based and linear models - p. 243 23. Chapter 8. Data transformations - p. 285 24. Chapter 9. Probabilistic methods - p. 335 25. Abstract - p. 335 26. 9.1 Foundations - p. 336 27. 9.2 Bayesian Networks - p. 339 28. 9.3 Clustering and Probability Density Estimation - p. 352 29. 9.4 Hidden Variable Models - p. 363 30. 9.5 Bayesian Estimation and Prediction - p. 367 31. 9.6 Graphical Models and Factor Graphs - p. 370 32. 9.7 Conditional Probability Models - p. 392 33. 9.8 Sequential and Temporal Models - p. 403 34. 9.9 Further Reading and Bibliographic Notes - p. 410 35. 9.10 Weka Implementations - p. 416 36. Chapter 10. Deep learning - p. 417 37. Abstract - p. 417 38. 10.1 Deep Feedforward Networks - p. 420 39. 10.2 Training and Evaluating Deep Networks - p. 431 40. 10.3 Convolutional Neural Networks - p. 437 41. 10.4 Autoencoders - p. 445 42. 10.5 Stochastic Deep Networks - p. 449 43. 10.6 Recurrent Neural Networks - p. 456 44. 10.7 Further Reading and Bibliographic Notes - p. 461 45. 10.8 Deep Learning Software and Network Implementations - p. 464 46. 10.9 WEKA Implementations - p. 466 47. Chapter 11. Beyond supervised and unsupervised learning - p. 467 48. Chapter 12. Ensemble learning - p. 479 49. Chapter 13. Moving on: applications and beyond - p. 503 50. Appendix A. Theoretical foundations - p. 533 51. Appendix B. The WEKA workbench - p. 553 52. References - p. 573 53. Index - p. 601
Data Mining: Practical Machine Learning Tools and Techniques, Fourth 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 fourth 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 substantial new chapters on probabilistic methods and on deep learning. Accompanying the book is a new version of the popular WEKA machine learning software from the University of Waikato. Authors Witten, Frank, Hall, and Pal include today's techniques coupled with the methods at the leading edge of contemporary research.
Please visit the book companion website at https://www.cs.waikato.ac.nz/~ml/weka/book.html.
It contains
1. Part I: Introduction to data mining - p. xxxii 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. Abstracts - p. 91 7. 4.1 Inferring Rudimentary Rules - p. 93 8. 4.2 Simple Probabilistic Modeling - p. 96 9. 4.3 Divide-and-Conquer: Constructing Decision Trees - p. 105 10. 4.4 Covering Algorithms: Constructing Rules - p. 113 11. 4.5 Mining Association Rules - p. 120 12. 4.6 Linear Models - p. 128 13. 4.7 Instance-Based Learning - p. 135 14. 4.8 Clustering - p. 141 15. 4.9 Multi-instance Learning - p. 156 16. 4.10 Further Reading and Bibliographic Notes - p. 158 17. 4.11 Weka Implementations - p. 160 18. Chapter 5. Credibility: Evaluating what’s been learned - p. 161 19. Part II: More advanced machine learning schemes - p. 203 20. Part II. More advanced machine learning schemes - p. 205 21. Chapter 6. Trees and rules - p. 209 22. Chapter 7. Extending instance-based and linear models - p. 243 23. Chapter 8. Data transformations - p. 285 24. Chapter 9. Probabilistic methods - p. 335 25. Abstract - p. 335 26. 9.1 Foundations - p. 336 27. 9.2 Bayesian Networks - p. 339 28. 9.3 Clustering and Probability Density Estimation - p. 352 29. 9.4 Hidden Variable Models - p. 363 30. 9.5 Bayesian Estimation and Prediction - p. 367 31. 9.6 Graphical Models and Factor Graphs - p. 370 32. 9.7 Conditional Probability Models - p. 392 33. 9.8 Sequential and Temporal Models - p. 403 34. 9.9 Further Reading and Bibliographic Notes - p. 410 35. 9.10 Weka Implementations - p. 416 36. Chapter 10. Deep learning - p. 417 37. Abstract - p. 417 38. 10.1 Deep Feedforward Networks - p. 420 39. 10.2 Training and Evaluating Deep Networks - p. 431 40. 10.3 Convolutional Neural Networks - p. 437 41. 10.4 Autoencoders - p. 445 42. 10.5 Stochastic Deep Networks - p. 449 43. 10.6 Recurrent Neural Networks - p. 456 44. 10.7 Further Reading and Bibliographic Notes - p. 461 45. 10.8 Deep Learning Software and Network Implementations - p. 464 46. 10.9 WEKA Implementations - p. 466 47. Chapter 11. Beyond supervised and unsupervised learning - p. 467 48. Chapter 12. Ensemble learning - p. 479 49. Chapter 13. Moving on: applications and beyond - p. 503 50. Appendix A. Theoretical foundations - p. 533 51. Appendix B. The WEKA workbench - p. 553 52. References - p. 573 53. Index - p. 601
