Local Pattern Detection

International Seminar, Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers

  • Katharina Morik
  • Jean-François Boulicaut
  • Arno Siebes
Conference proceedings

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3539)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 3539)

Table of contents

  1. Front Matter
  2. Francesco Bonchi, Fosca Giannotti
    Pages 1-19
  3. David J. Hand, Niall M. Adams, Nick A. Heard
    Pages 39-52
  4. Frank Höppner
    Pages 53-70
  5. Nada Lavrač, Filip Železný, Sašo Džeroski
    Pages 71-88
  6. Dunja Mladenic, Marko Grobelnik
    Pages 89-97
  7. Katharina Morik, Hanna Köpcke
    Pages 98-114
  8. Céline Rouveirol, Francois Radvanyi
    Pages 135-152
  9. Stefan Rüping
    Pages 153-170
  10. Myra Spiliopoulou, Steffan Baron
    Pages 190-206
  11. Claus Weihs, Uwe Ligges
    Pages 217-231
  12. Back Matter

About these proceedings


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 the new ?eld knowledge discovery in databases (KDD) at the intersection of machine learning, statistics and database technology. Being interdisciplinary by nature, the ?eld o?ers the opportunity to combine the expertise of di?erent ?elds intoacommonobjective.Moreover,withineach?elddiversemethodshave been developed and justi?ed with respect to di?erent quality criteria. We have toinvestigatehowthesemethods cancontributeto solvingthe problemofKDD. Traditionally, KDD was seeking to ?nd global models for the data that - plain 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 coverageor classi?cation models like decision trees or linear decision functions. In practice, though, the use of these models often is very l- ited, because global models tend to ?nd only the obvious patterns in the data, 1 which domain experts already are aware of . What is really of interest to the users are the local patterns that deviate from the already-known background knowledge. David Hand, who organized a workshop in 2002, proposed the new ?eld of local patterns.


algorithmic learning algorithms calculus data analysis data mining learning pattern detection pattern discovery pattern mining pattern searches text mining usage pattern detection

Editors and affiliations

  • Katharina Morik
    • 1
  • Jean-François Boulicaut
    • 2
  • Arno Siebes
    • 3
  1. 1.Computer Science VIII, artificial Intelligence UnitTechnische Universität DortmundDortmundGermany
  2. 2.INSA-LyonLIRIS CNRS UMR5205VilleurbanneFrance
  3. 3.Department of Computer ScienceUniversiteit Utrecht 

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-26543-6
  • Online ISBN 978-3-540-31894-1
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • Buy this book on publisher's site