Local Pattern Detection International Seminar, Dagstuhl Castle, Germ
Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scienti?c and commercial information. The need to analyze these masses of data has led to the evolution of
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Subseries of Lecture Notes in Computer Science
3539
Katharina Morik Jean-François Boulicaut Arno Siebes (Eds.)
Local Pattern Detection International Seminar Dagstuhl Castle, Germany, April 12-16, 2004 Revised Selected Papers
13
Series Editors Jaime G. Carbonell, Carnegie Mellon University, Pittsburgh, PA, USA Jörg Siekmann, University of Saarland, Saarbrücken, Germany Volume Editors Katharina Morik University of Dortmund, Computer Science Department, LS VIII 44221 Dortmund, Germany E-mail: [email protected] Jean-François Boulicaut INSA Lyon, LIRIS CNRS UMR 5205 Batiment Blaise Pascal 69621 Villeurbanne, France E-mail: [email protected] Arno Siebes Utrecht University Department of Information and Computing Sciences PO Box 80.089, 3508TB Utrecht, The Netherlands E-mail: [email protected]
Library of Congress Control Number: 2005929338
CR Subject Classification (1998): I.2, H.2.8, F.2.2, E.5, G.3, H.3 ISSN ISBN-10 ISBN-13
0302-9743 3-540-26543-0 Springer Berlin Heidelberg New York 978-3-540-26543-6 Springer Berlin Heidelberg New York
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Preface
Introduction The dramatic increase in available computer storage capacity over the last 10 years has led to the creation of very large databases of scientific and commercial information. The need to analyze these masses of data has led to the evolution of the new field knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the field offers the opportunity to combine the expertise of different fields into a common objective. Moreover, within each field diverse methods have been developed and justified with respect to different quality criteria. We have to investigate how these methods can contribute to solving the problem of KDD. Traditionally, KDD was seeking to find global models for the data that explain most of the instances of the database and describe the general structure of the data. Examples are statistical time series models, cluster models, logic programs with high coverage or classification models like decision trees or linear decision functions. In practice, though, the use of these models often is very lim
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