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Multi-Objective Evolutionary Algorithms for Knowledge Discovery from Databases

  • Ashish Ghosh
  • Satchidananda Dehuri
  • Susmita Ghosh

Part of the Studies in Computational Intelligence book series (SCI, volume 98)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Ricardo Landa-Becerra, Luis V. Santana-Quintero, Carlos A. Coello Coello
    Pages 23-46
  3. Hisao Ishibuchi, Isao Kuwajima, Yusuke Nojima
    Pages 47-70
  4. Yaochu Jin, Bernhard Sendhoff, Edgar Körner
    Pages 71-90
  5. R. Alcalá, J. Alcalá-Fdez, M. J. Gacto, F. Herrera
    Pages 91-107
  6. M. N. Murty, Babaria Rashmin, Chiranjib Bhattacharyya
    Pages 137-159

About this book

Introduction

Data Mining (DM) is the most commonly used name to describe such computational analysis of data and the results obtained must conform to several objectives such as accuracy, comprehensibility, interest for the user etc. Though there are many sophisticated techniques developed by various interdisciplinary fields only a few of them are well equipped to handle these multi-criteria issues of DM. Therefore, the DM issues have attracted considerable attention of the well established multiobjective genetic algorithm community to optimize the objectives in the tasks of DM.

The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.

Keywords

Knowledge Discovery Form Databases algorithm algorithms calculus classification clustering data mining database databases evolution evolutionary algorithm genetic algorithms knowledge discovery neural networks optimization

Editors and affiliations

  • Ashish Ghosh
    • 1
  • Satchidananda Dehuri
    • 2
  • Susmita Ghosh
    • 3
  1. 1.Indian Statistical InstituteKolkataIndia
  2. 2.F. M. UniversityBalasoreIndia
  3. 3.Jadavpur UniversityKolkataIndia

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-77467-9
  • Copyright Information Springer Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-77466-2
  • Online ISBN 978-3-540-77467-9
  • Series Print ISSN 1860-949X
  • Buy this book on publisher's site