Automating the Design of Data Mining Algorithms

An Evolutionary Computation Approach

By Gisele L. Pappa , Alex Freitas

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This unique text seeks to automate the design of a data mining algorithm. It first overviews data mining and evolutionary algorithms then discusses the design of a new genetic programming system for automating the design of full rule induction algorithms.

Full Description

  • ISBN13: 978-3-6420-2540-2
  • 204 Pages
  • User Level: Science
  • Publication Date: October 27, 2009
  • Available eBook Formats: PDF
  • eBook Price: $129.00
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Full Description
Traditionally, evolutionary computing techniques have been applied in the area of data mining either to optimize the parameters of data mining algorithms or to discover knowledge or patterns in the data, i.e., to directly solve the target data mining problem. This book proposes a different goal for evolutionary algorithms in data mining: to automate the design of a data mining algorithm, rather than just optimize its parameters. The authors first offer introductory overviews on data mining, focusing on rule induction methods, and on evolutionary algorithms, focusing on genetic programming. They then examine the conventional use of evolutionary algorithms to discover classification rules or related types of knowledge structures in the data, before moving to the more ambitious objective of their research, the design of a new genetic programming system for automating the design of full rule induction algorithms. They analyze computational results from their automatically designed algorithms, which show that the machine-designed rule induction algorithms are competitive when compared with state-of-the-art human-designed algorithms. Finally the authors examine future research directions. This book will be useful for researchers and practitioners in the areas of data mining and evolutionary computation.
Table of Contents

Table of Contents

  1. Introduction.
  2. Data Mining.
  3. Evolutionary Algorithms.
  4. Evolutionary Algorithms for Automating the Parameter Setting and the Partial Design of Data Mining Algorithms.
  5. A New Grammar
  6. based Genetic Programming System for Automating the Design of Full Rule Induction Algorithms.
  7. Computational Results on the Automatic Design of Full Induction Algorithms.
  8. Conclusions.
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