Perner p (ed) advances in data mining LNCS 3275 (,2005)(t)(183s)

Lecture Notes in Artificial Intelligence Edited by J G Carbonell and J Siekmann Subseries of Lecture Notes in Computer Science 3275 This page intentionally left blank Petra Perner (Ed.) Advances in Data Mining Applications in Image Mining, Medicine and Biotechnology, Management and Environmental Control, and Telecommunications 4th Industrial Conference on Data Mining, ICDM 2004 Leipzig, Germany, July 4-7, 2004 Revised Selected Papers Springer eBook ISBN: Print ISBN: 3-540-30185-2 3-540-24054-3 ©2005 Springer Science + Business Media, Inc Print ©2004 Springer-Verlag Berlin Heidelberg All rights reserved No part of this eBook may be reproduced or transmitted in any form or by any means, electronic, mechanical, recording, or otherwise, without written consent from the Publisher Created in the United States of America Visit Springer's eBookstore at: and the Springer Global Website Online at: http://ebooks.springerlink.com http://www.springeronline.com Preface The Industrial Conference on Data Mining ICDM-Leipzig was the fourth meeting in a series of annual events which started in 2000, organized by the Institute of Computer Vision and Applied Computer Sciences (IBaI) in Leipzig The mission of the conference is to bring together researchers and people from industry in order to discuss together new trends and applications in data mining This year a broad spectrum of work of different applications was presented ranging from image mining, medicine and biotechnology, management and environmental control, to telecommunications Besides that an industrial exhibition showed the successful application of data mining methods by industries in different areas such as medical devices, mass data management systems, data mining tools, etc During the discussion many projects were inspired leading to new and joint work The fruitful discussions, the exchange of ideas and the spirit of the conference made it a remarkable event for both sides, industry and research We would like to express our appreciation to the reviewers for their precise and highly professional work We appreciate the help and understanding of the editorial staff at Springer and in particular Alfred Hofmann, who supported the publication of these proceedings in the LNAI series Last, but not least, we wish to thank all speakers, participants and industrial exhibitors who contributed to the success of the conference We are looking forward to welcoming you to ICDM 2005 (www.data-miningforum.de) and to the new work you will present there July 2004 Petra Perner This page intentionally left blank Table of Contents Case-Based Reasoning Neuro-symbolic System for Business Internal Control Juan M Corchado, M Lourdes Borrajo, María A Pellicer, J Carlos đez Applying Case Based Reasoning Approach in Analyzing Organization Change Management Data Orit Raphaeli, Jacob Zahavi, Ron Kenett 11 Improving the K-NN Classification with the Euclidean Distance Through Linear Data Transformations Leon Bobrowski, Magdalena Topczewska 23 An IBR System to Quantify the Ocean’s Carbon Dioxide Budget Juan M Corchado, Emilio S Corchado, Jim Aiken 33 A Beta-Cooperative CBR System for Constructing a Business Management Model Emilio S Corchado, Juan M Corchado, Lourdes Sáiz, Ana Lara 42 Image Mining Braving the Semantic Gap: Mapping Visual Concepts from Images and Videos Da Deng 50 Mining Images to Find General Forms of Biological Objects Petra Perner, Horst Perner, Angela Bühring, Silke Jänichen 60 Applications in Process Control and Insurance The Main Steps to Data Quality Joachim Schmid 69 Cost-Sensitive Design of Claim Fraud Screens Stijn Viaene, Dirk Van Gheel, Mercedes Ayuso, Montserrat Guillén 78 An Early Warning System for Vehicle Related Quality Data Matthias Grabert, Markus Prechtel, Tomas Hrycej, Winfried Günther 88 VIII Table of Contents Clustering and Association Rules Shape-Invariant Cluster Validity Indices Greet Frederix, Eric J Pauwels 96 Mining Indirect Association Rules Shinichi Hamano, Masako Sato 106 An Association Mining Method for Time Series and Its Application in the Stock Prices of TFT-LCD Industry Chiung-Fen Huang, Yen-Chu Chen, An-Pin Chen 117 Clustering of Web Sessions Using Levenstein Metric Andrei Scherbina, Sergey Kuznetsov 127 Telecommunication A Data Mining Approach for Call Admission Control and Resource Reservation in Wireless Mobile Networks Sherif Rashad, Mehmed Kantardzic, Anup Kumar 134 Mining of an Alarm Log to Improve the Discovery of Frequent Patterns Franỗoise Fessant, Fabrice Clộrot, Christophe Dousson 144 Medicine and Biotechnology Feature Selection and Classification Model Construction on Type Diabetic Patient’s Data Yue Huang, Paul McCullagh, Norman Black, Roy Harper 153 Knowledge Based Phylogenetic Classification Mining Isabelle Bichindaritz, Stephen Potter, Sociộtộ Franỗaise de Systộmatique 163 Author Index 173 Neuro-symbolic System for Business Internal Control Juan M Corchado1, M Lourdes Borrajo2, María A Pellicer1, and J Carlos đez3 Deparatamento de Informática y Automática, University of Salamanca, Plaza de la Merced s/n, 37008 Salamanca, Spain corchado@usal.es Department of Computer Science, University of Vigo, Campus As Lagoas, s/n, 32004 Ourense, Spain Department of Financial Accounting, University of Vigo, Campus As Lagoas, s/n, 32004 Ourense, Spain {lborrajo,jcyanez}@uvigo.es Abstract The complexity of current organization systems, and the increase in importance of the realization of internal controls in firms, make it necessary to construct models that automate and facilitate the work of auditors An intelligent system has been developed to automate the internal control process This system is composed of two case-based reasoning systems The objective of the system is to facilitate the process of internal auditing in small and medium firms from the textile sector The system, analyses the data that characterises each one of the activities carried out by the firm, then determines the state of each activity, calculates the associated risk, detects the erroneous processes, and generates recommendations to improve these processes As such, the system is a useful tool for the internal auditor in order to make decisions based on the risk generated Each one of the case-based reasoning systems that integrates the system uses a different problem solving method in each of the steps of the reasoning cycle: fuzzy clustering during the retrieval phase, a radial basis function network and a multi-criterion discreet method during the reuse phase and a rule based system for recommendation generation The system has been proven successfully in several small and medium companies in the textile sector, located in the northwest of Spain The accuracy of the technologies employed in the system has been demonstrated by the results obtained over the last two years Introduction Nowadays, organization systems employed in enterprises are increasing in complexity Moreover, in recent years, the number of regulatory norms has increased considerably As a consequence of this, the need has arisen for periodic internal audits But the evaluation and the prediction of the evolution of these types of systems, characterized by their great dynamism, are, in general, complicated It is necessary to construct models that facilitate analysis work carried out in changing environments, such as finance P Perner (Ed.): ICDM 2004, LNAI 3275, pp 1–10, 2004 © Springer-Verlag Berlin Heidelberg 2004 160 Y Huang et al trol continues to deteriorate with time – so it is likely that those on “Insulin Treatment” would have the worst overall blood sugar control “Insulin Treatment” was selected as the best predictor for classifying blood sugar control This again makes clinical sense Fig Classification Accuracy of Different Data Mining Algorithms based on 10-CV Fig Classification Accuracy of Different Data Mining Algorithms based on Testing Data Overall there was high concordance between the features selected using data mining techniques and the factors anticipated as being important by the diabetes expert The models’ high best predictive performance and the clinical relevance of the features selected suggest that decision support and prediction will be achievable with further refinements Conclusion and Future Work The paper reports on a study, which constructed outcome prediction models from a database of 2,017 Type diabetic patients The following conclusions can be drawn: Feature Selection and Classification Model Construction 161 Irrelevant features will reduce the performance (including efficiency and classification accuracy) of the practical classifiers under study; Feature selection enhances the performance of classification algorithms; “Age” is the most important attribute for both diabetic control; Younger patients (