Automating the Design of Data Mining Algorithms

1. Front Matter

   Pages I-XIII

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2. Introduction

   • Gisele L. Pappa, Alex A. Freitas

   Pages 1-16

3. Data Mining

   • Gisele L. Pappa, Alex A. Freitas

   Pages 17-46

4. Evolutionary Algorithms

   • Gisele L. Pappa, Alex A. Freitas

   Pages 47-84

5. Genetic Programming for Classification and Algorithm Design

   • Gisele L. Pappa, Alex A. Freitas

   Pages 85-108

6. Automating the Design of Rule Induction Algorithms

   • Gisele L. Pappa, Alex A. Freitas

   Pages 109-135

7. Computational Results on the Automatic Design of Full Rule Induction Algorithms

   • Gisele L. Pappa, Alex A. Freitas

   Pages 137-175

8. Directions for Future Research on the Automatic Design of Data Mining Algorithms

   • Gisele L. Pappa, Alex A. Freitas

   Pages 177-184

9. Back Matter

   Pages 185-187

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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.

• Evolutionary algorithms
• Evolutionary computing
• Genetic programming
• Machine learning
• Rule induction
• algorithms
• classification
• data mining
• tar
• data structures

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)

• Depto. Ciência da Computação, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

  Gisele L. Pappa

• Computing Laboratory, University of Kent, Canterbury, United Kingdom

  Alex Freitas

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