Table of contents

  1. Front Matter
  2. Timo Lampinen, Mikko Laurikkala, Hannu Koivisto, Tapani Honkanen
    Pages 15-27
  3. Attila Gyenesei, Jukka Teuhola
    Pages 73-89
  4. Chengqi Zhang, Jeffrey Xu Yu, Shichao Zhang
    Pages 91-112
  5. Guillaume Becq, Sylvie Charbonnier, Florian Chapotot, Alain Buguet, Lionel Bourdon, Pierre Baconnier
    Pages 113-127
  6. Ken McGarry, Andrew Martin, Dale Addison
    Pages 175-189
  7. Ajith Abraham, Ravi Jain
    Pages 191-207
  8. G.E.M.D.C. Bandara, R.M. Ranawana, S.D. Pathirana
    Pages 209-232
  9. Xiaozhe Wang, Ajith Abraham, Kate A. Smith
    Pages 233-250
  10. Shyue-Liang Wang, Wei-Shuo Lo, Tzung-Pei Hong
    Pages 251-266
  11. Chang-Shing Lee, Hei-Chia Wang, Meng-Ju Chang
    Pages 267-282
  12. K. Blackmore, T. Bossomaier, S. Foy, D. Thomson
    Pages 305-314
  13. Malka N. Halgamuge, Siddeswara M. Guru, Andrew Jennings
    Pages 315-331

About this book


Knowledge Discovery today is a significant study and research area. In finding answers to many research questions in this area, the ultimate hope is that knowledge can be extracted from various forms of data around us. This book covers recent advances in unsupervised and supervised data analysis methods in Computational Intelligence for knowledge discovery. In its first part the book provides a collection of recent research on distributed clustering, self organizing maps and their recent extensions. If labeled data or data with known associations are available, we may be able to use supervised data analysis methods, such as classifying neural networks, fuzzy rule-based classifiers, and decision trees. Therefore this book presents a collection of important methods of supervised data analysis. "Classification and Clustering for Knowledge Discovery" also includes variety of applications of knowledge discovery in health, safety, commerce, mechatronics, sensor networks, and telecommunications.


Extension classification clustering communication computational intelligence data analysis decision tree fuzzy intelligence knowledge knowledge discovery mechatronics neural network neural networks uncertainty

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin/Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-26073-8
  • Online ISBN 978-3-540-32404-1
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
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