IT training intelligent data mining ruan, chen, kerre wets 2005 09 29

Da Ruan, Guoqing Chen, Etienne E Kerre, Geert Wets (Eds.) Intelligent Data Mining Studies in Computational Intelligence, Volume Editor-in-chief Prof Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul Newelska 01-447 Warsaw Poland E-mail: kacprzyk@ibspan.waw.pl Further volumes of this series can be found on our homepage: springeronline.com Vol Tetsuya Hoya Artificial Mind System – Kernel Memory Approach, 2005 ISBN 3-540-26072-2 Vol Saman K Halgamuge, Lipo Wang (Eds.) Computational Intelligence for Modelling and Prediction, 2005 ISBN 3-540-26071-4 Vol Boz˙ ena Kostek Perception-Based Data Processing in Acoustics, 2005 ISBN 3-540-25729-2 Vol Saman Halgamuge, Lipo Wang (Eds.) Classification and Clustering for Knowledge Discovery, 2005 ISBN 3-540-26073-0 Vol 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 Professor Dr Etienne E Kerre Belgian Nuclear Research Center (SCK· CEN) Boeretang 200, 2400 Mol Belgium E-mail: druan@sckcen.be 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 Professor Dr Geert Wets School of Economics and Management, Division MIS Tsinghua University 100084 Beijing The People’s Republic of China E-mail: chengq@mail.tsinghua.edu.cn 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 This work is subject to copyright All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer Violations are liable for prosecution under the German Copyright Law Springer is a part of Springer Science+Business Media springeronline.com c Springer-Verlag Berlin Heidelberg 2005 Printed in The Netherlands The use of general descriptive names, registered names, trademarks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use Typesetting: by the authors and TechBooks using a Springer LATEX macro package Printed on acid-free paper SPIN: 11004011 55/TechBooks 543210 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 possible and even indispensable support of IT-tools in this context is obvious Because of the large data repositories many firms maintain nowadays, an important 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 visualization 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 papers 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 intelligent systems and data mining, applications in economic and management, industrial engineering and other related industrial applications The main objective of this book is to gather a number of peer-reviewed high quality contri- VI Preface butions in the relevant topic areas The focus is especially on those chapters that provide theoretical/analytical solutions to the problems of real interest in intelligent techniques possibly combined with other traditional tools, for data mining and the corresponding applications to engineers and managers of different industrial sectors Academic and applied researchers and research students working on data mining can also directly benefit from this book The volume is divided into three logical parts containing 24 chapters written by 62 authors from 10 countries1 in the field of data mining in conjunction with intelligent systems Part on Intelligent Systems and Data Mining contains nine chapters that contribute to a deeper understanding of theoretical background and methodologies to be used in data mining Part on Economic and Management Applications collects six chapters that dedicate to the key issue of real-world economic and management applications Part presents nine chapters on Industrial Engineering Applications that also point out the future research direction on the topic of intelligent data mining We would like to thank all the contributors for their kind cooperation to this book; and especially to Prof Janusz Kacprzyk (Editor-in-chief of Studies in Computational Intelligence) and Dr Thomas Ditzinger of Springer for their advice and help during the production phases of this book The support from the China Flanders project (grant No BIL 00/46) is greatly appreciated April 2005 Da Ruan Guoqing Chen Etienne E Kerre Geert Wets Australia, Belgium, Bulgaria, China, Greece, France, Turkey, Spain, the UK, and the USA Corresponding Authors The corresponding authors for all contributions are indicated with their email addresses under the titles of chapters Intelligent Data Mining Techniques and Applications Editors: Da Ruan (The Belgian Nuclear Research Centre, Mol, Belgium) (druan@sckcen.be) Guoqing Chen (Tsinghua University, Beijing, China) Etienne E Kerre (Ghent University, Gent, Belgium) Geert Wets (Limburg University, Diepenbeek, Belgium) Editors’ preface D Ruan druan@sckcen.be, G Chen, E.E Kerre, G Wets Part I: Intelligent Systems and Data Mining Some Considerations in Multi-Source Data Fusion R.R Yager yager@panix.com Granular Nested Causal Complexes L.J Mazlack mazlack@uc.edu Gene Regulating Network Discovery Y Cao vc23@ee.duke.edu, P.P Wang, A Tokuta VIII Corresponding Authors Semantic Relations and Information Discovery D Cai caid@dcs.gla.ac.uk, C.J van Rijsbergen Sequential Pattern Mining T Li trli@swjtu.edu.cn, Y Xu, D Ruan, W.-M Pan Uncertain Knowledge Association Through Information Gain A Tocatlidou atocat@aua.gr, D Ruan, S.Th Kaloudis, N.A Lorentzos Data Mining for Maximal Frequency Patterns in Sequence Group J.W Guan J.Guan@qub.ac.uk, D.A Belle, D.Y Liu Mining Association Rule with Rough Sets J.W Guan j.guan@qub.ac.uk, D.A Belle, D.Y Liu The Evolution of the Concept of Fuzzy Measure L Garmendia lgarmend@fdi.ucm.es Part II: Economic and Management Applications Building ER Models with Association Rules M De Cock martine.decock@ugent.be, C Cornelis, M Ren, G.Q Chen, E.E Kerre Discovering the Factors Affecting the Location Selection of FDI in China L Zhang zhangl34@em.tsinghua.edu.cn, Y Zhu, Y Liu, N Zhou, G.Q Chen Penalty-Reward Analysis with Uninorms: A Study of Customer (Dis)Satisfaction K Vanhoof koen.vanhoof@luc.ac.be, P Pauwels, J Dombi, T Brijs, G Wets Using an Adapted Classification Based on Associations Algorithm in an Activity-Based Transportation System D Janssens Davy.janssens@luc.ac.be, G Wets, T Brijs, K Vanhoof Evolutionary Induction of Descriptive Rules in a Market Problem M.J del Jesus, P Gonz´ alez, F Herrera herrera@decsai.ugr.es, M Mesonero Personalized Multi-Layer Decision Support in Reverse Logistics Management J Lu jielu@it.uts.edu.au, G Zhang Corresponding Authors IX Part III: Industrial Engineering Applications Fuzzy Process Control with Intelligent Data Mining M Gă ulbay gulbaym@itu.edu.tr, C Kahraman Accelerating the New Product Introduction with Intelligent Data Mining G Bă uyă ukă ozkan, gbuyukozkan@gsu.edu.tr, O Feyzioglu Integrated Clustering Modeling with Backpropagation Neural Network for Efficient Customer Relationship Management Mining T Ertay ertay@atlas.cc.itu.edu.tr, B Cekyay Sensory Quality Management and Assessment: from Manufacturers to Consumers L Koehl ludovic.koehl@ensait.fr, X Zeng, B Zhou, Y Ding Simulated Annealing Approach for the Multi-Objective Facility Layout Problem U.R Tuzkaya, T Ertay ertay@atlas.cc.itu.edu.tr, D Ruan Self-Tuning Fuzzy Rule Bases with Belief Structure J Liu j.liu@ulster.ac.uk, D Ruan, J.-B Yang, L Martinez A User Centred Approach to Management Decision Making L.P Maguire lp.maguire@ulster.ac.uk, T.A McCloskey, P.K Humphreys, R McIvor Techniques to Improve Multi-Agent Systems for Searching and Mining the Web E Herrera-Viedma, C Porcel, F Herrera, L Martinez herrera@decsai.ugr.es, A.G Lopez-Herrera Advanced Simulator Data Mining for Operators’ Performance Assessment A.J Spurgin, G.I Petkov gip@mail.orbitel.bg, Subject Index (druan@sckcen.be) ... 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... University, Beijing, China) Etienne E Kerre (Ghent University, Gent, Belgium) Geert Wets (Limburg University, Diepenbeek, Belgium) Editors’ preface D Ruan druan@sckcen.be, G Chen, E.E Kerre, G Wets. .. the sum of its compatibilities with the input Credibility Weighted Sources In the preceding we have implicitly assumed all the data had the same credibility Here we shall consider the situation