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Da Ruan, Guoqing Chen, Etienne E. Kerre, Geert Wets (Eds.)
Intelligent Data Mining
Studies in Computational Intelligence, Volume 5
Editor-in-chief
Prof. Janusz Kacprzyk
Systems Research Institute
Polish Academy of Sciences
ul. Newelska 6
01-447 Warsaw
Poland
E-mail: kacprzyk@ibspan.waw.pl
Further volumes of this series
can be found on our homepage:
springeronline.com
Vo l . 1. Tetsuya Hoya
Artificial Mind System – Kernel Memory
Approach, 2005
ISBN 3-540-26072-2
Vo l . 2. Saman K. Halgamuge, Lipo Wang
(Eds.)
Computational Intelligence for Modelling
and Prediction, 2005
ISBN 3-540-26071-4
Vo l . 3.Bozena Kostek
Perception-Based Data Processing in
Acoustics, 2005
ISBN 3-540-25729-2
Vo l . 4. Saman Halgamuge, Lipo Wang (Eds.)
Classification and Clustering for Knowledge
Discovery, 2005
ISBN 3-540-26073-0
Vo l . 5. Da Ruan, Guoqing Chen, Etienne E.
Kerre, Geert Wets (Eds.)
Intelligent Data Mining, 2005
ISBN 3-540-26256-3
Da Ruan
Guoqing Chen
Etienne E. Kerre
Geert Wets
(Eds.)
Intelligent Data Mining
Techniques and Applications
ABC
Professor Dr. Da Ruan
Belgian Nuclear Research
Center (SCK · CEN)
Boeretang 200, 2400 Mol
Belgium
E-mail: druan@sckcen.be
Professor Dr. Etienne E. Kerre
Department of Applied Mathematics
and Computer Science
Ghent University
Krijgslaan 281 (S9), 9000 Gent
Belgium
E-mail: etienne.kerre@ugent.be
Professor Dr. Guoqing Chen
School of Economics
and Management, Division MIS
Tsinghua University
100084 Beijing
The People’s Republic of China
E-mail: chengq@mail.tsinghua.edu.cn
Professor Dr. Geert Wets
Limburg University Centre
Universiteit Hasselt
3590 Diepenbeek
Belgium
E-mail: geert.wets@uhasselt.be
Library of Congress Control Number: 2005927317
ISSN print edition: 1860-949X
ISSN electronic edition: 1860-9503
ISBN-10 3-540-26256-3 Springer Berlin Heidelberg New York
ISBN-13 978-3-540-26256-5 Springer Berlin Heidelberg New York
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Preface
In today’s information-driven economy, companies may benefit a lot from
suitable information management. Although information management is not
just a technology-based concept rather a business practice in general, the pos-
sible and even indispensable support of IT-tools in this context is obvious.
Because of the large data repositories many firms maintain nowadays, an im-
portant role is played by data mining techniques that find hidden, non-trivial,
and potentially useful information from massive data sources. The discovered
knowledge can then be further processed in desired forms to support business
and scientific decision making.
Data mining (DM) is also known as Knowledge Discovery in Databases.
Following a formal definition by W. Frawley, G. Piatetsky-Shapiro and C.
Matheus (in AI Magazine, Fall 1992, pp. 213–228), DM has been defined as
“The nontrivial extraction of implicit, previously unknown, and potentially
useful information from data.” It uses machine learning, statistical and vi-
sualization techniques to discover and present knowledge in a form that is
easily comprehensible to humans. Since the middle of 1990s, DM has been
developed as one of the hot research topics within computer sciences, AI and
other related fields. More and more industrial applications of DM have been
recently realized in today’s IT time.
The root of this book was originally based on a joint China-Flanders
project (2001–2003) on methods and applications of knowledge discovery to
support intelligent business decisions that addressed several important issues
of concern that are relevant to both academia and practitioners in intelligent
systems. Extensive contributions were made possible from some selected pa-
pers of the 6th International FLINS conference on Applied Computational
Intelligence (2004).
Intelligent Data Mining – Techniques and Applications is an organized
edited collection of contributed chapters covering basic knowledge for intel-
ligent systems and data mining, applications in economic and management,
industrial engineering and other related industrial applications. The main ob-
jective of this book is to gather a number of peer-reviewed high quality contri-

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