Optimization Based Data Mining: Theory and Applications

By Yong Shi , Yingjie Tian , Gang Kou , Yi Peng , Jianping Li

Optimization Based Data Mining: Theory and Applications Cover Image

Optimization techniques have been widely adopted to implement various data mining algorithms. This book focuses on cutting-edge theoretical developments and real-life applications in optimization, covering a range of fields from finance to bioinformatics.

Full Description

  • ISBN13: 978-0-8572-9503-3
  • 331 Pages
  • User Level: Science
  • Publication Date: May 16, 2011
  • Available eBook Formats: PDF
  • eBook Price: $99.00
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Full Description
Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery.Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.
Table of Contents

Table of Contents

  1. Support Vector Machines for Classification Problems.
  2. Method of Maximum Margin.
  3. Dual Problem.
  4. Soft Margin.
  5. C
  6. Support Vector Classification.
  7. C
  8. Support Vector Classification with Nominal Attributes.
  9. LOO Bounds for Support Vector Machines.
  10. Introduction.
  11. LOO bounds for ε−Support Vector Regression.
  12. LOO Bounds for Support Vector Ordinal Regression Machine .
  13. Support Vector Machines for Multi
  14. class Classification Problems.
  15. K
  16. class Linear Programming Support Vector Classification Regression Machine (KLPSVCR).
  17. Support Vector Ordinal Regression Machine for Multi
  18. class Problems.
  19. Unsupervised and Semi
  20. Supervised Support Vector Machines.
  21. Unsupervised and Semi
  22. Supervised ν
  23. Support Vector Machine.
  24. Numerical Experiments.
  25. Unsupervised and Semi
  26. supervised Lagrange Support Vector Machine.
  27. Unconstrained Transductive Support Vector Machine.
  28. Robust Support Vector Machines.
  29. Support Vector Ordinal Regression Machine.
  30. Robust Multi
  31. class Algorithm.
  32. Robust Unsupervised and Semi
  33. Supervised Bounded C
  34. Support Vector Machine.
  35. Feature Selection via lp
  36. norm Support Vector Machines.
  37. lp
  38. norm Support Vector Classification.
  39. lp
  40. norm Proximal Support Vector Machine.
  41. Multiple Criteria Linear Programming.
  42. Comparison of Support Vector Machine and Multiple Criteria Programming.
  43. Multiple Criteria Linear Programming.
  44. Multiple Criteria Linear Programming for Multiple Classes.
  45. Penalized Multiple Criteria Linear Programming.
  46. Regularized Multiple Criteria Linear Programs for Classification.
  47. MCLP Extensions.
  48. Fuzzy MCLP.
  49. FMCLP with Soft Constraints.
  50. FMCLP by Tolerances.
  51. Kernel based MCLP.
  52. Knowledge based MCLP.
  53. Rough set based MCLP.
  54. Regression by MCLP.
  55. Multiple Criteria Quadratic Programming.
  56. A General Multiple Mathematical Programming.
  57. Multi
  58. criteria Convex Quadratic Programming Model Kernel based MCQP.
  59. Non
  60. additiveMCLP.
  61. Non
  62. additiveMeasures and Integrals.
  63. Non
  64. additive Classification Models.
  65. Non
  66. additive MCP.
  67. Reducing the time complexity.
  68. Hierarchical Choquet integral.
  69. Choquetintegral with respect to k
  70. additive measure.
  71. MC2LP.
  72. MC2LP Classification.
  73. Minimal Error and Maximal Between
  74. class Variance Model.
  75. Firm Financial Analysis.
  76. Finance and Banking.
  77. General Classification Process.
  78. Firm Bankruptcy Prediction.
  79. Personal Credit Management.
  80. Credit Card Accounts Classification.
  81. Two
  82. class Analysis.
  83. FMCLP Analysis.
  84. Three
  85. class Analysis.
  86. Four
  87. class Analysis.
  88. Empirical Study and Managerial Significance of Four
  89. class Models.
  90. Health Insurance Fraud Detection.
  91. Problem Identification.
  92. A Real
  93. life Data Mining Study.
  94. Network Intrusion Detection.
  95. Problem and Two Datasets.
  96. Classify NeWT Lab Data by MCMP, MCMP with kernel and See5.
  97. Classify KDDCUP
  98. Data by Nine Different Methods.
  99. Internet Service Analysis.
  100. VIP Mail Dataset.
  101. Empirical Study of Cross
  102. validation.
  103. Comparison of Multiple
  104. Criteria Programming Models and SVM.
  105. HIV
  106. 1 Informatics.
  107. HIV
  108. 1 Mediated Neuronal Dendritic and Synaptic Damage.
  109. Materials and Methods.
  110. Designs of Classifications.
  111. Analytic Results.
  112. Anti
  113. gen and Anti
  114. body Informatics.
  115. Problem Background.
  116. MCQP,LDA and DT Analyses.
  117. Kernel
  118. based MCQP and SVM Analyses.
  119. Geol
  120. chemical Analyses.
  121. Problem Description.
  122. Multiple
  123. class Analyses.
  124. More Advanced Analyses.
  125. Intelligent Knowledge Management.
  126. Purposes of the Study.
  127. Definitions and Theoretical Framework of Intelligent Knowledge.
  128. Some Research Directions.
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