Automating the Design of Data Mining Algorithms: An Evolutionary Computation Approach / Edition 1

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"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.

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Editorial Reviews

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"The book is targeted at researchers and postgraduate students. As the amount of data being mined continues to grow it demands ever more sophisticated mining algorithms. Therefore there is a need for new algorithms and so Pappa and Freitas’ book will be of interest particularly to researchers in data mining. ... [T]his book will appeal to the target audience of [the journal] Genetic Programming and Evolvable Machines and, I feel, will align with the research interests of its readership." (John Woodward, Genetic Programming and Evolvable Machines (2011) 12:81–83)

“The book will be useful for postgraduate students and researchers in the data mining field and in evolutionary computation.” (Florin Gorunescu, Zentralblatt MATH, Vol. 1183, 2010)

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Product Details

  • ISBN-13: 9783642025402
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 10/1/2009
  • Series: Natural Computing Series
  • Edition description: 2010
  • Edition number: 1
  • Pages: 187
  • Product dimensions: 6.20 (w) x 9.20 (h) x 0.80 (d)

Table of Contents

1 Introduction 1

2 Data Mining 17

3 Evolutionary Algorithms 47

4 Genetic Programming for Classification and Algorithm Design 85

5 Automating the Design of Rule Induction Algorithms 109

6 Computational Results on the Automatic Design of Full Rule Induction Algorithms 137

7 Directions for Future Research on the Automatic Design of Data Mining Algorithms 177

Index 185

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