Data Mining and Knowledge Discovery with Evolutionary Algorithms

Freitas, Alex A. (2002) Data Mining and Knowledge Discovery with Evolutionary Algorithms. Natural Computing Series . Spinger-Verlag, Berlin, 265 pp. ISBN 3-540-43331-7. (The full text of this publication is not available from this repository)

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Abstract

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, interesting knowledge, which is potentially useful for 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. 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.

Item Type: Book
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 17:59
Last Modified: 20 May 2014 10:22
Resource URI: http://kar.kent.ac.uk/id/eprint/13669 (The current URI for this page, for reference purposes)
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