Optimization Based Data Mining: Theory and Applications

  • Yong Shi
  • Yingjie Tian
  • Gang Kou
  • Yi Peng
  • Jianping Li

Part of the Advanced Information and Knowledge Processing book series (AI&KP)

Table of contents

  1. Front Matter
    Pages I-XV
  2. Support Vector Machines: Theory and Algorithms

    1. Front Matter
      Pages 1-1
    2. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 3-13
    3. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 15-46
    4. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 47-60
    5. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 61-79
    6. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 81-105
    7. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 107-116
  3. Multiple Criteria Programming: Theory and Algorithms

    1. Front Matter
      Pages 117-117
    2. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 119-132
    3. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 133-156
    4. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 157-170
    5. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 171-181
    6. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 183-192
  4. Applications in Various Fields

    1. Front Matter
      Pages 193-193
    2. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 195-201
    3. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 203-231
    4. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 233-235
    5. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 237-241
    6. Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li
      Pages 243-248

About this book

Introduction

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.

Keywords

Convex Optimization Data Mining Machine Learning Multiple Criteria Linear Programming Optimization Statistical Learning Theory Support Vector Machines

Authors and affiliations

  • Yong Shi
    • 1
  • Yingjie Tian
    • 2
  • Gang Kou
    • 3
  • Yi Peng
    • 4
  • Jianping Li
    • 5
  1. 1., Research Cntr on Fict. Econ. & Data Sci.Chinese Academy of SciencesBeijingChina, People's Republic
  2. 2., Research Cntr on Fict. Econ. & Data Sci.Chinese Academy of SciencesBeijingChina, People's Republic
  3. 3., School of Management and EconomicsUniversity of Electr. Science & Technol.ChengduChina, People's Republic
  4. 4., School of Managment and EconomicsUniversity of Electr. Science & Technol.ChengduChina, People's Republic
  5. 5., Institute of Policy and ManagementChinese Academy of SciencesBeijingChina, People's Republic

Bibliographic information

  • DOI https://doi.org/10.1007/978-0-85729-504-0
  • Copyright Information Springer-Verlag London Limited 2011
  • Publisher Name Springer, London
  • eBook Packages Computer Science
  • Print ISBN 978-0-85729-503-3
  • Online ISBN 978-0-85729-504-0
  • Series Print ISSN 1610-3947
  • About this book