Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets.
After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Data, measurements, and data preprocessing - p. 23 3. Abstract - p. 23 4. 2.1 Data types - p. 24 5. 2.2 Statistics of data - p. 27 6. 2.3 Similarity and distance measures - p. 43 7. 2.4 Data quality, data cleaning, and data integration - p. 55 8. 2.5 Data transformation - p. 63 9. 2.6 Dimensionality reduction - p. 71 10. 2.7 Summary - p. 78 11. 2.8 Exercises - p. 80 12. 2.9 Bibliographic notes - p. 83 13. Bibliography - p. 84 14. Chapter 3. Data warehousing and online analytical processing - p. 85 15. Chapter 4. Pattern mining: basic concepts and methods - p. 145 16. Chapter 5. Pattern mining: advanced methods - p. 175 17. Abstract - p. 175 18. 5.1 Mining various kinds of patterns - p. 175 19. 5.2 Mining compressed or approximate patterns - p. 187 20. 5.3 Constraint-based pattern mining - p. 191 21. 5.4 Mining sequential patterns - p. 198 22. 5.5 Mining subgraph patterns - p. 211 23. 5.6 Pattern mining: application examples - p. 223 24. 5.7 Summary - p. 232 25. 5.8 Exercises - p. 233 26. 5.9 Bibliographic notes - p. 235 27. Bibliography - p. 237 28. Chapter 6. Classification: basic concepts and methods - p. 239 29. Abstract - p. 239 30. 6.1 Basic concepts - p. 239 31. 6.2 Decision tree induction - p. 243 32. 6.3 Bayes classification methods - p. 259 33. 6.4 Lazy learners (or learning from your neighbors) - p. 266 34. 6.5 Linear classifiers - p. 269 35. 6.6 Model evaluation and selection - p. 277 36. 6.7 Techniques to improve classification accuracy - p. 290 37. 6.8 Summary - p. 298 38. 6.9 Exercises - p. 299 39. 6.10 Bibliographic notes - p. 302 40. Bibliography - p. 305 41. Chapter 7. Classification: advanced methods - p. 307 42. Abstract - p. 307 43. 7.1 Feature selection and engineering - p. 307 44. 7.2 Bayesian belief networks - p. 315 45. 7.3 Support vector machines - p. 318 46. 7.4 Rule-based and pattern-based classification - p. 327 47. 7.5 Classification with weak supervision - p. 342 48. 7.6 Classification with rich data type - p. 351 49. 7.7 Potpourri: other related techniques - p. 359 50. 7.8 Summary - p. 369 51. 7.9 Exercises - p. 370 52. 7.10 Bibliographic notes - p. 374 53. Bibliography - p. 377 54. Chapter 8. Cluster analysis: basic concepts and methods - p. 379 55. Abstract - p. 379 56. 8.1 Cluster analysis - p. 379 57. 8.2 Partitioning methods - p. 385 58. 8.3 Hierarchical methods - p. 394 59. 8.4 Density-based and grid-based methods - p. 407 60. 8.5 Evaluation of clustering - p. 417 61. 8.6 Summary - p. 425 62. 8.7 Exercises - p. 427 63. 8.8 Bibliographic notes - p. 429 64. Bibliography - p. 430 65. Chapter 9. Cluster analysis: advanced methods - p. 431 66. Abstract - p. 431 67. 9.1 Probabilistic model-based clustering - p. 431 68. 9.2 Clustering high-dimensional data - p. 441 69. 9.3 Biclustering - p. 447 70. 9.4 Dimensionality reduction for clustering - p. 454 71. 9.5 Clustering graph and network data - p. 463 72. 9.6 Semisupervised clustering - p. 475 73. 9.7 Summary - p. 479 74. 9.8 Exercises - p. 480 75. 9.9 Bibliographic notes - p. 482 76. Bibliography - p. 483 77. Chapter 10. Deep learning - p. 485 78. Abstract - p. 485 79. 10.1 Basic concepts - p. 485 80. 10.2 Improve training of deep learning models - p. 500 81. 10.3 Convolutional neural networks - p. 517 82. 10.4 Recurrent neural networks - p. 526 83. 10.5 Graph neural networks - p. 539 84. 10.6 Summary - p. 547 85. 10.7 Exercises - p. 548 86. 10.8 Bibliographic notes - p. 552 87. Bibliography - p. 556 88. Chapter 11. Outlier detection - p. 557 89. Chapter 12. Data mining trends and research frontiers - p. 605 90. Appendix A. Mathematical background - p. 655 91. 1.1 Probability and statistics - p. 655 92. 1.2 Numerical optimization - p. 661 93. 1.3 Matrix and linear algebra - p. 668 94. 1.4 Concepts and tools from signal processing - p. 673 95. 1.5 Bibliographic notes - p. 678 96. Bibliography - p. 681 97. Index - p. 735
Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets.
After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining.
1. Chapter 1. Introduction - p. 1 2. Chapter 2. Data, measurements, and data preprocessing - p. 23 3. Abstract - p. 23 4. 2.1 Data types - p. 24 5. 2.2 Statistics of data - p. 27 6. 2.3 Similarity and distance measures - p. 43 7. 2.4 Data quality, data cleaning, and data integration - p. 55 8. 2.5 Data transformation - p. 63 9. 2.6 Dimensionality reduction - p. 71 10. 2.7 Summary - p. 78 11. 2.8 Exercises - p. 80 12. 2.9 Bibliographic notes - p. 83 13. Bibliography - p. 84 14. Chapter 3. Data warehousing and online analytical processing - p. 85 15. Chapter 4. Pattern mining: basic concepts and methods - p. 145 16. Chapter 5. Pattern mining: advanced methods - p. 175 17. Abstract - p. 175 18. 5.1 Mining various kinds of patterns - p. 175 19. 5.2 Mining compressed or approximate patterns - p. 187 20. 5.3 Constraint-based pattern mining - p. 191 21. 5.4 Mining sequential patterns - p. 198 22. 5.5 Mining subgraph patterns - p. 211 23. 5.6 Pattern mining: application examples - p. 223 24. 5.7 Summary - p. 232 25. 5.8 Exercises - p. 233 26. 5.9 Bibliographic notes - p. 235 27. Bibliography - p. 237 28. Chapter 6. Classification: basic concepts and methods - p. 239 29. Abstract - p. 239 30. 6.1 Basic concepts - p. 239 31. 6.2 Decision tree induction - p. 243 32. 6.3 Bayes classification methods - p. 259 33. 6.4 Lazy learners (or learning from your neighbors) - p. 266 34. 6.5 Linear classifiers - p. 269 35. 6.6 Model evaluation and selection - p. 277 36. 6.7 Techniques to improve classification accuracy - p. 290 37. 6.8 Summary - p. 298 38. 6.9 Exercises - p. 299 39. 6.10 Bibliographic notes - p. 302 40. Bibliography - p. 305 41. Chapter 7. Classification: advanced methods - p. 307 42. Abstract - p. 307 43. 7.1 Feature selection and engineering - p. 307 44. 7.2 Bayesian belief networks - p. 315 45. 7.3 Support vector machines - p. 318 46. 7.4 Rule-based and pattern-based classification - p. 327 47. 7.5 Classification with weak supervision - p. 342 48. 7.6 Classification with rich data type - p. 351 49. 7.7 Potpourri: other related techniques - p. 359 50. 7.8 Summary - p. 369 51. 7.9 Exercises - p. 370 52. 7.10 Bibliographic notes - p. 374 53. Bibliography - p. 377 54. Chapter 8. Cluster analysis: basic concepts and methods - p. 379 55. Abstract - p. 379 56. 8.1 Cluster analysis - p. 379 57. 8.2 Partitioning methods - p. 385 58. 8.3 Hierarchical methods - p. 394 59. 8.4 Density-based and grid-based methods - p. 407 60. 8.5 Evaluation of clustering - p. 417 61. 8.6 Summary - p. 425 62. 8.7 Exercises - p. 427 63. 8.8 Bibliographic notes - p. 429 64. Bibliography - p. 430 65. Chapter 9. Cluster analysis: advanced methods - p. 431 66. Abstract - p. 431 67. 9.1 Probabilistic model-based clustering - p. 431 68. 9.2 Clustering high-dimensional data - p. 441 69. 9.3 Biclustering - p. 447 70. 9.4 Dimensionality reduction for clustering - p. 454 71. 9.5 Clustering graph and network data - p. 463 72. 9.6 Semisupervised clustering - p. 475 73. 9.7 Summary - p. 479 74. 9.8 Exercises - p. 480 75. 9.9 Bibliographic notes - p. 482 76. Bibliography - p. 483 77. Chapter 10. Deep learning - p. 485 78. Abstract - p. 485 79. 10.1 Basic concepts - p. 485 80. 10.2 Improve training of deep learning models - p. 500 81. 10.3 Convolutional neural networks - p. 517 82. 10.4 Recurrent neural networks - p. 526 83. 10.5 Graph neural networks - p. 539 84. 10.6 Summary - p. 547 85. 10.7 Exercises - p. 548 86. 10.8 Bibliographic notes - p. 552 87. Bibliography - p. 556 88. Chapter 11. Outlier detection - p. 557 89. Chapter 12. Data mining trends and research frontiers - p. 605 90. Appendix A. Mathematical background - p. 655 91. 1.1 Probability and statistics - p. 655 92. 1.2 Numerical optimization - p. 661 93. 1.3 Matrix and linear algebra - p. 668 94. 1.4 Concepts and tools from signal processing - p. 673 95. 1.5 Bibliographic notes - p. 678 96. Bibliography - p. 681 97. Index - p. 735
