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Data Mining and Knowledge Discovery Handbook

By Oded Maimon , Lior Rokach

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Updated and revised, Data Mining and Knowledge Discovery Handbook, 2nd Edition, presents the concepts, theories, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD). The text includes over 25 new topics, new case studies based on real-word examples, and more.

Full Description

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  • ISBN13: 978-0-3870-9822-7
  • 1305 Pages
  • User Level: Science
  • Publication Date: September 10, 2010
  • Available eBook Formats: PDF
Full Description
Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data.Data Mining and Knowledge Discovery Handbook, Second Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This handbook first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.Data Mining and Knowledge Discovery Handbook, Second Edition is designed for research scientists, libraries and advanced-level students in computer science and engineering as a reference. This handbook is also suitable for professionals in industry, for computing applications, information systems management, and strategic research management.
Table of Contents

Table of Contents

  1. New Added Topics: Graph Mining.
  2. Sequence Mining.
  3. Utility
  4. Based Data Mining.
  5. Swarm Intelligence.
  6. Privacy Preserving DM.
  7. Multimedia Data Mining.
  8. Data Streaming Mining.
  9. Data Mining in Bioinformatics.
  10. Ontology Mining.
  11. Reliability Issues of Knowledge Discovery.
  12. Optimization
  13. based Data Mining.
  14. Distributed Data Mining.
  15. Standards for Data Mining.
  16. The Clementine Software.
  17. The SAS Miner. All other topics updated to cover developments in the field: Introduction to knowledge discovery in databases.
  18. Part I Preprocessing methods.
  19. Data cleansing.
  20. Handling missing attribute values.
  21. Geometric methods for feature extraction and dimensional reduction.
  22. Dimension Reduction and feature selection.
  23. Discretization methods.
  24. outlier detection.
  25. Part II Supervised methods.
  26. Introduction to supervised methods.
  27. Decision trees.
  28. Bayesian networks.
  29. Data mining within a regression framework.
  30. Support vector machines.
  31. Part III Unsupervised methods.
  32. Clustering methods.
  33. Association rules.
  34. Frequent set mining.
  35. Constraint
  36. based data mining.
  37. Link analysis.
  38. Part IV Soft computing methods.
  39. Evolutionary algorithms for data mining.
  40. Reinforcement
  41. learning: an overview from a data mining perspective.
  42. Neural networks.
  43. Granular computing and rough sets.
  44. Part V Supporting methods.
  45. Statistical methods for data mining.
  46. Logics for data mining.
  47. Wavelet methods in data mining.
  48. Fractal mining.
  49. Interestingness measures.
  50. Quality assessment approaches in data mining.
  51. Data mining model comparison.
  52. Data mining query languages.
  53. Part VI Advanced methods.
  54. Meta
  55. learning.
  56. Bias vs variance decomposition for regression and classification.
  57. Mining with rare cases.
  58. Mining data streams.
  59. Mining high
  60. dimensional data.
  61. Text mining and information extraction.
  62. Spatial data mining.
  63. Data mining for imbalanced datasets: an overview.
  64. Relational data mining.
  65. Web mining.
  66. A review of web document clustering approaches.
  67. Causal discovery.
  68. Ensemble methods for classifiers.
  69. Decomposition methodology for knowledge discovery and data mining.
  70. Information fusion.
  71. Parallel and grid
  72. based data mining.
  73. Collaborative data mining.
  74. Organizational data mining.
  75. Mining time series data.
  76. Part VII Applications.
  77. Data mining in medicine.
  78. Learning information patterns in biological databases.
  79. Data mining for selection of manufacturing processes.
  80. Data mining in telecommunications.
  81. Data mining for financial applications.
  82. Data mining for intrusion detection.
  83. Data mining for software testing.
  84. Data mining for CRM.
  85. Data mining for target marketing.
  86. Part VIII Software.
  87. GainSmarts data mining system for marketing.
  88. Index.

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