© 2002

Data Mining and Knowledge Discovery with Evolutionary Algorithms


Part of the Natural Computing Series book series (NCS)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Alex A. Freitas
    Pages 1-11
  3. Alex A. Freitas
    Pages 13-43
  4. Alex A. Freitas
    Pages 45-63
  5. Alex A. Freitas
    Pages 65-78
  6. Alex A. Freitas
    Pages 79-106
  7. Alex A. Freitas
    Pages 107-137
  8. Alex A. Freitas
    Pages 139-163
  9. Alex A. Freitas
    Pages 165-178
  10. Alex A. Freitas
    Pages 179-204
  11. Alex A. Freitas
    Pages 255-261
  12. Back Matter
    Pages 263-265

About this book


This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an area of active research. In general, data mining consists of extracting knowledge from data. In this book we particularly emphasize the importance of discovering comprehensible and interesting knowledge, which is potentially useful to the reader for intelligent decision making. In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions (rules or another form of knowledge representation). In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search.
This book presents a comprehensive review of basic concepts on both data mining and evolutionary algorithms and discusses significant advances in the integration of these two areas. It is self-contained, explaining both basic concepts and advanced topics.


Artificial Intelligence Computing Methodologies Data Mining Evolutionary Algorithms Fuzzy Machine Learning algorithms evolutionary algorithm genetic algorithms genetic programming knowledge discovery

Authors and affiliations

  1. 1.Computing LaboratoryUniversity of KentCanterburyUK

Bibliographic information

Industry Sectors
IT & Software
Finance, Business & Banking


From the reviews:

"In the snappily-titled Data Mining and Knowledge Discovery with Evolutionary Algorithms, leading researcher Alex A Freitas introduces both data mining and evolutionary algorithms. … The aim is to introduce and address the key challenges to a high level of detail. With an understanding gleaned from this book, and source code available freely on the web, the world of data mining is your oyster." (Application Development Advisor, January/February, 2003)