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 Paper

181 Pages·2005·3.46 MB·English

by  Juan M. Corchado, M. Lourdes Borrajo, María A. Pellicer, J. Carlos Yáñez (auth.), Petra Perner (eds.)

Lecture Notes in Artificial Intelligence 3275
EditedbyJ.G.CarbonellandJ.Siekmann
Subseries of Lecture Notes in Computer Science
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
1 3
SeriesEditors
JaimeG.Carbonell,CarnegieMellonUniversity,Pittsburgh,PA,USA
JörgSiekmann,UniversityofSaarland,Saarbrücken,Germany
VolumeEditor
PetraPerner
InstituteofComputerVisionandAppliedComputerSciences
Körnerstr.10,04107Leipzig,Germany
E-mail:[email protected]
LibraryofCongressControlNumber:2004116339
CRSubjectClassification(1998):I.2.6,I.2,H.2.8,K.4.4,J.3,I.4,J.6,J.1
ISSN0302-9743
ISBN3-540-24054-3SpringerBerlinHeidelbergNewYork
Thisworkissubjecttocopyright.Allrightsarereserved,whetherthewholeorpartofthematerialis
concerned,specificallytherightsoftranslation,reprinting,re-useofillustrations,recitation,broadcasting,
reproductiononmicrofilmsorinanyotherway,andstorageindatabanks.Duplicationofthispublication
orpartsthereofispermittedonlyundertheprovisionsoftheGermanCopyrightLawofSeptember9,1965,
initscurrentversion,andpermissionforusemustalwaysbeobtainedfromSpringer.Violationsareliable
toprosecutionundertheGermanCopyrightLaw.
SpringerisapartofSpringerScience+BusinessMedia
springeronline.com
©Springer-VerlagBerlinHeidelberg2004
PrintedinGermany
Typesetting:Camera-readybyauthor,dataconversionbyScientificPublishingServices,Chennai,India
Printedonacid-freepaper SPIN:11365860 06/3142 543210
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-mining-
forum.de) and to the new work you will present there.
July 2004 Petra Perner

Table of Contents
Case-Based Reasoning
Neuro-symbolic System for Business Internal Control
Juan M. Corchado, M. Lourdes Borrajo, María A. Pellicer,
J. Carlos Yáñez……………………………………………………………….. …1
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
Françoise Fessant, Fabrice Clérot, Christophe Dousson………………..….. 144
Medicine and Biotechnology
Feature Selection and Classification Model Construction on Type 2
Diabetic Patient's Data
Yue Huang, Paul McCullagh, Norman Black, Roy Harper……………..…… 153
Knowledge Based Phylogenetic Classification Mining
Isabelle Bichindaritz, Stephen Potter, Société Française de Systématique…. 163
Author Index…………………………………………………………………… 173
Neuro-symbolic System for Business Internal Control
Juan M. Corchado1 , M. Lourdes Borrajo2, María A. Pellicer1, and J. Carlos Yáñez3
1 Deparatamento de Informática y Automática,
University of Salamanca, Plaza de la Merced s/n, 37008 Salamanca, Spain
[email protected]
2 Department of Computer Science,
University of Vigo, Campus As Lagoas, s/n, 32004 Ourense, Spain
3 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.
1 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. P erner (Ed.): ICDM 2004, LNAI 3275, pp. 1–10, 2004.
© Springer-Verlag Berlin Heidelberg 2004
2 J.M. Corchado et al.
The processes carried out inside a firm can be included in functional areas [19].
Each one of these areas is denominated a “Function”. A Function is a group of
coordinated and related activities, which are necessary to reach the objectives of the
firm and are carried out in a systematic and reiterated way [11]. Functions are divided
into activities, which are associated to well defined objectives. The functions that are
usually carried out within a firm are: Purchases, Cash Management, Sales,
Information Technology, Fixed Assets Management, Compliance to Legal Norms and
Human Resources.
In turn, each one of these functions is broken down into a series of activities. For
example, the function Information Technology is divided in the following areas:
Computer Plan Development, Study of Systems, Installation of Systems, Treatment of
Information Flows and Security Management.
Each activity is composed of a number of tasks, for example: register, authorise,
approve, harmonise, separate obligations, operate, etc. Control procedures are
established in the tasks to assure that the established objectives are achieved. Rule-
based systems (RBS) have traditionally been used with the purpose of delimiting the
audit decision-making tasks [6]. However, Messier and Hansen [13] found many
situations in which auditors resolved problems by referring to previous situations.
This contrasts with the very nature of RBS systems, since they have very little
capacity for extracting information from past experience and present problems in
order to adapt to changes in the environment.
In contrast, case based reasoning systems (CBR) are able to relate past experiences
or cases to current observations, solving new problems through the memorization and
adaptation of previously tested solutions. This is an effective way of learning, similar
to the general structure of human thought. CBR systems are especially suitable when
the rules that define a knowledge system are difficult to obtain, or the number and
complexity of the rules is too large to create an expert system. Moreover, CBR
systems have the capacity to update their memory dynamically, based on new
information (new cases), as well as, improving the resolution of problems [14].
However, in problems like those presented in this study, standard techniques of
monitoring and prediction cannot be applied due to the complexity of the problem, the
existence of certain preliminary knowledge, the great dynamism of the system, etc. In
these types of systems it is necessary to use models that combine the advantages of
several mechanisms of problem-solving capable to of resolving specific parts of the
general problem and attending other parts.
In this sense, an adaptive system has been developed. The system possesses the
flexibility to behave in different ways and to evolve, depending on the environment in
which it operates. The developed system is composed of two fundamental
subsystems:
(cid:131) Subsystem IEA (Identification of the State of the Activity) whose objectives are:
1. to identify the state or situation of each one of activities of the company.
2. to calculate the risk associated with this state.
(cid:131) Subsystem GR (Generation of Recommendations), whose goal is:
1. generation of recommendations from the detection of inconsistent processes.
These recommendations will allow the positive evolution of the internal
processes of the company.