Overview
- 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.
- Includes supplementary material: sn.pub/extras
Part of the book series: Natural Computing Series (NCS)
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Table of contents(7 chapters)
About this book
Reviews
From the reviews:
"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)
Authors and Affiliations
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Depto. Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil
Gisele L. Pappa
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Computing Laboratory, University of Kent, Canterbury, United Kingdom
Alex Freitas
Bibliographic Information
Book Title: Automating the Design of Data Mining Algorithms
Book Subtitle: An Evolutionary Computation Approach
Authors: Gisele L. Pappa, Alex Freitas
Series Title: Natural Computing Series
DOI: https://doi.org/10.1007/978-3-642-02541-9
Publisher: Springer Berlin, Heidelberg
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2010
Hardcover ISBN: 978-3-642-02540-2Published: 10 November 2009
Softcover ISBN: 978-3-642-26125-1Published: 14 March 2012
eBook ISBN: 978-3-642-02541-9Published: 27 October 2009
Series ISSN: 1619-7127
Series E-ISSN: 2627-6461
Edition Number: 1
Number of Pages: XIII, 187
Number of Illustrations: 33 b/w illustrations
Topics: Data Mining and Knowledge Discovery, Data Structures, Artificial Intelligence