Knowledge Discovery in Inductive Databases 5th International Worksho
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Editorial Board David Hutchison Lancaster University, UK Takeo Kanade Carnegie Mellon University, Pittsburgh, PA, USA Josef Kittler University of Surrey, Guildford, UK Jon M. Kleinberg Cornell University, Ithaca, NY, USA Friedemann Mattern ETH Zurich, Switzerland John C. Mitchell Stanford University, CA, USA Moni Naor Weizmann Institute of Science, Rehovot, Israel Oscar Nierstrasz University of Bern, Switzerland C. Pandu Rangan Indian Institute of Technology, Madras, India Bernhard Steffen University of Dortmund, Germany Madhu Sudan Massachusetts Institute of Technology, MA, USA Demetri Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Moshe Y. Vardi Rice University, Houston, TX, USA Gerhard Weikum Max-Planck Institute of Computer Science, Saarbruecken, Germany
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Sašo Džeroski Jan Struyf (Eds.)
Knowledge Discovery in Inductive Databases 5th International Workshop, KDID 2006 Berlin, Germany, September 18, 2006 Revised Selected and Invited Papers
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Volume Editors Sašo Džeroski Jožef Stefan Institute Department of Knowledge Technologies Jamova 39, 1000 Ljubljana, Slovenia E-mail: [email protected] Jan Struyf Katholieke Universiteit Leuven Department of Computer Science Celestijnenlaan 200A, 3001 Leuven, Belgium E-mail: [email protected]
Library of Congress Control Number: 2007937944 CR Subject Classification (1998): H.2, I.2 LNCS Sublibrary: SL 3 – Information Systems and Application, incl. Internet/Web and HCI ISSN ISBN-10 ISBN-13
0302-9743 3-540-75548-9 Springer Berlin Heidelberg New York 978-3-540-75548-7 Springer Berlin Heidelberg New York
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Preface
The 5th International Workshop on Knowledge Discovery in Inductive Databases (KDID 2006) was held on September 18, 2006 in Berlin, Germany, in conjunction with ECML/PKDD 2006: The 17th European Conference on Machine Learning (ECML) and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD). Inductive databases (IDBs) represent a database view on data mining and knowledge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an
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