Data Mining with Computational Intelligence - Lipo Wang , Xiuju Fu.pdf

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Advanced Information and Knowledge Processing
Lipo Wang
·
Xiuju Fu
Data Mining with
Computational Intelligence
With 72 Figures and 65 Tables
123
Lipo Wang
Nanyang Technological University
School of Electrical and Electronical Engineering
Block S1, Nanyang Avenue,
639798 Singapore, Singapore
elpwang@ntu.edu.sg
Xiuju Fu
Institute of High Performance Computing,
Software and Computing, Science Park 2,
The Capricorn
Science Park Road 01-01
117528 Singapore, Singapore
fuxj@pmail.ntu.edu.sg
Series Editors
Xindong Wu
Lakhmi Jain
Library of Congress Control Number: 200528948
ACM Computing Classification (1998): H.2.8., I.2
ISBN-10 3-540-24522-7 Springer Berlin Heidelberg New York
ISBN-13 978-3-540-24522-3 Springer Berlin Heidelberg New York
This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned,
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© Springer-Verlag Berlin Heidelberg 2005
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Preface
Nowadays data accumulate at an alarming speed in various storage devices,
and so does valuable information. However, it is difficult to understand in-
formation hidden in data without the aid of data analysis techniques, which
has provoked extensive interest in developing a field separate from machine
learning. This new field is data mining.
Data mining has successfully provided solutions for finding information
from data in bioinformatics, pharmaceuticals, banking, retail, sports and en-
tertainment, etc. It has been one of the fastest growing fields in the computer
industry. Many important problems in science and industry have been ad-
dressed by data mining methods, such as neural networks, fuzzy logic, decision
trees, genetic algorithms, and statistical methods.
This book systematically presents how to utilize fuzzy neural networks,
multi-layer perceptron (MLP) neural networks, radial basis function (RBF)
neural networks, genetic algorithms (GAs), and support vector machines
(SVMs) in data mining tasks. Fuzzy logic mimics the imprecise way of reason-
ing in natural languages and is capable of tolerating uncertainty and vague-
ness. The MLP is perhaps the most popular type of neural network used
today. The RBF neural network has been attracting great interest because
of its locally tuned response in RBF neurons like biological neurons and its
global approximation capability. This book demonstrates the power of GAs in
feature selection and rule extraction. SVMs are well known for their excellent
accuracy and generalization abilities.
We will describe data mining systems which are composed of data pre-
processing, knowledge-discovery models, and a data-concept description. This
monograph will enable both new and experienced data miners to improve their
practices at every step of data mining model design and implementation.
Specifically, the book will describe the state of the art of the following
topics, including both work carried out by the authors themselves and by
other researchers:

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