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Evolutionary Computation in Data Mining

  • Ashish Ghosh
  • Lakhmi C. Jain

Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 163)

Table of contents

  1. Front Matter
    Pages I-XVII
  2. Jose Ramon Cano, Francisco Herrera, Manuel Lozano
    Pages 21-39
  3. Qi Yu, Kay Chen Tan, Tong Heng Lee
    Pages 101-123
  4. Minh Ha Nguyen, Hussein Abbass, Robert McKay
    Pages 125-156
  5. Olfa Nasraoui, Elizabeth Leon, Raghu Krishnapuram
    Pages 157-188
  6. W. B. Langdon, S. J. Barrett
    Pages 211-235
  7. Po-Chang (P.C.) Ko, Ping-Chen (P.C.) Lin
    Pages 249-263
  8. Back Matter
    Pages 264-265

About this book

Introduction

This carefully edited book reflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications. "Evolutionary Computation in Data Mining" provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and bioinformatics.

Keywords

Data mining Evolutionary Computation Knowledge Discovery in Databases Multi-Agent Data mining algorithm algorithms bioinformatics databases evolutionary algorithm genetic programming knowledge discovery programming

Editors and affiliations

  • Ashish Ghosh
    • 1
  • Lakhmi C. Jain
    • 2
  1. 1.Machine Intelligence UnitIndian Statistical InstituteKolkataIndia
  2. 2.Knowledge-Based Intelligent, Engineering Systems CentreUniversity of South AustraliaAdelaideAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-32358-9
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-22370-2
  • Online ISBN 978-3-540-32358-7
  • Series Print ISSN 1434-9922
  • Buy this book on publisher's site