Automating the Design of Data Mining Algorithms
An Evolutionary Computation Approach
Authors: Pappa, Gisele L., Freitas, Alex A.
Free Preview- 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.
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- About this book
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Data mining is a very active research area with many successful real-world app- cations. It consists of a set of concepts and methods used to extract interesting or useful knowledge (or patterns) from real-world datasets, providing valuable support for decision making in industry, business, government, and science. Although there are already many types of data mining algorithms available in the literature, it is still dif cult for users to choose the best possible data mining algorithm for their particular data mining problem. In addition, data mining al- rithms have been manually designed; therefore they incorporate human biases and preferences. This book proposes a new approach to the design of data mining algorithms. - stead of relying on the slow and ad hoc process of manual algorithm design, this book proposes systematically automating the design of data mining algorithms with an evolutionary computation approach. More precisely, we propose a genetic p- gramming system (a type of evolutionary computation method that evolves c- puter programs) to automate the design of rule induction algorithms, a type of cl- si cation method that discovers a set of classi cation rules from data. We focus on genetic programming in this book because it is the paradigmatic type of machine learning method for automating the generation of programs and because it has the advantage of performing a global search in the space of candidate solutions (data mining algorithms in our case), but in principle other types of search methods for this task could be investigated in the future.
- Reviews
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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)
- Table of contents (7 chapters)
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Introduction
Pages 1-16
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Data Mining
Pages 17-46
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Evolutionary Algorithms
Pages 47-84
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Genetic Programming for Classification and Algorithm Design
Pages 85-108
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Automating the Design of Rule Induction Algorithms
Pages 109-135
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Table of contents (7 chapters)
- Download Preface 1 PDF (68.5 KB)
- Download Sample pages 2 PDF (343.2 KB)
- Download Table of contents PDF (57.8 KB)
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Bibliographic Information
- Bibliographic Information
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- Book Title
- Automating the Design of Data Mining Algorithms
- Book Subtitle
- An Evolutionary Computation Approach
- Authors
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- Gisele L. Pappa
- Alex A. Freitas
- Series Title
- Natural Computing Series
- Copyright
- 2010
- Publisher
- Springer-Verlag Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- eBook ISBN
- 978-3-642-02541-9
- DOI
- 10.1007/978-3-642-02541-9
- Hardcover ISBN
- 978-3-642-02540-2
- Softcover ISBN
- 978-3-642-26125-1
- Series ISSN
- 1619-7127
- Edition Number
- 1
- Number of Pages
- XIII, 187
- Number of Illustrations
- 33 b/w illustrations
- Topics
