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Data Mining and Knowledge Discovery with Evolutionary Algorithms

  • Book
  • © 2002

Overview

Authors:
  1. Alex A. Freitas

    1. Computing Laboratory, University of Kent, Canterbury, UK

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Part of the book series: Natural Computing Series (NCS)

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Table of contents (12 chapters)

  1. Front Matter

    Pages i-xiv
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  2. Introduction

    • Alex A. Freitas
    Pages 1-11

  3. Data Mining Tasks and Concepts

    • Alex A. Freitas
    Pages 13-43

  4. Data Mining Paradigms

    • Alex A. Freitas
    Pages 45-63

  5. Data Preparation

    • Alex A. Freitas
    Pages 65-78

  6. Basic Concepts of Evolutionary Algorithms

    • Alex A. Freitas
    Pages 79-106

  7. Genetic Algorithms for Rule Discovery

    • Alex A. Freitas
    Pages 107-137

  8. Genetic Programming for Rule Discovery

    • Alex A. Freitas
    Pages 139-163

  9. Evolutionary Algorithms for Clustering

    • Alex A. Freitas
    Pages 165-178

  10. Evolutionary Algorithms for Data Preparation

    • Alex A. Freitas
    Pages 179-204

  11. Evolutionary Algorithms for Discovering Fuzzy Rules

    • Alex A. Freitas
    Pages 205-231

  12. Scaling up Evolutionary Algorithms for Large Data Sets

    • Alex A. Freitas
    Pages 233-254

  13. Conclusions and Research Directions

    • Alex A. Freitas
    Pages 255-261

  14. Back Matter

    Pages 263-265
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Keywords

About this book

This book addresses the integration of two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increas­ ingly popular in the last few years, and their integration is currently an area of active research. In essence, data mining consists of extracting valid, comprehensible, and in­ teresting knowledge from data. Data mining is actually an interdisciplinary field, since there are many kinds of methods that can be used to extract knowledge from data. Arguably, data mining mainly uses methods from machine learning (a branch of artificial intelligence) and statistics (including statistical pattern recog­ nition). Our discussion of data mining and evolutionary algorithms is primarily based on machine learning concepts and principles. In particular, in this book we emphasize the importance of discovering comprehensible, interesting knowledge, which the user can potentially use to make intelligent decisions. 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 knowl­ edge 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.

Reviews

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)

Authors and Affiliations

  • Computing Laboratory, University of Kent, Canterbury, UK

    Alex A. Freitas

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