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

by Gisele L. Pappa, Alex A. Freitas
     
 

ISBN-10: 3642025404

ISBN-13: 9783642025402

Pub. Date: 10/01/2009

Publisher: Springer Berlin Heidelberg

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

Overview

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.

Product Details

ISBN-13:
9783642025402
Publisher:
Springer Berlin Heidelberg
Publication date:
10/01/2009
Series:
Natural Computing Series
Edition description:
2010
Pages:
187
Product dimensions:
6.20(w) x 9.20(h) x 0.80(d)

Table of Contents

Data Mining.- Evolutionary Algorithms.- Genetic Programming for Classification and Algorithm Design.- Automating the Design of Rule Induction Algorithms.- Computational Results on the Automatic Design of Full Rule Induction Algorithms.- Directions for Future Research on the Automatic Design of Data Mining Algorithms.

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