Knowledge Discovery in Inductive Databases

4th International Workshop, KDID 2005, Porto, Portugal, October 3, 2005, Revised Selected and Invited Papers

  • Francesco Bonchi
  • Jean-François Boulicaut
Conference proceedings KDID 2005

Part of the Lecture Notes in Computer Science book series (LNCS, volume 3933)

Table of contents

  1. Front Matter
  2. Invited Papers

  3. Contributed Papers

    1. Maurizio Atzori, Paolo Mancarella, Franco Turini
      Pages 38-54
    2. Jérémy Besson, Ruggero G. Pensa, Céline Robardet, Jean-François Boulicaut
      Pages 55-71
    3. Toon Calders, Bart Goethals
      Pages 86-103
    4. Tao-Yuan Jen, Dominique Laurent, Nicolas Spyratos, Oumar Sy
      Pages 104-123
    5. Stefan Kramer, Volker Aufschild, Andreas Hapfelmeier, Alexander Jarasch, Kristina Kessler, Stefan Reckow et al.
      Pages 124-138
    6. Siegfried Nijssen, Joost N. Kok
      Pages 165-187
    7. Csaba István Sidló, András Lukács
      Pages 188-201
    8. Arnaud Soulet, Bruno Crémilleux
      Pages 202-221
    9. Jan Struyf, Sašo Džeroski
      Pages 222-233
    10. Bernard Ženko, Sašo Džeroski, Jan Struyf
      Pages 234-250
  4. Back Matter

About these proceedings


The4thInternationalWorkshoponKnowledgeDiscoveryinInductiveDatabases (KDID 2005) was held in Porto, Portugal, on October 3, 2005 in conjunction with the 16th European Conference on Machine Learning and the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases. Ever since the start of the ?eld of data mining, it has been realized that the integration of the database technology into knowledge discovery processes was a crucial issue. This vision has been formalized into the inductive database perspective introduced by T. Imielinski and H. Mannila (CACM 1996, 39(11)). The main idea is to consider knowledge discovery as an extended querying p- cess for which relevant query languages are to be speci?ed. Therefore, inductive databases might contain not only the usual data but also inductive gener- izations (e. g. , patterns, models) holding within the data. Despite many recent developments, there is still a pressing need to understand the central issues in inductive databases. Constraint-based mining has been identi?ed as a core technology for inductive querying, and promising results have been obtained for rather simple types of patterns (e. g. , itemsets, sequential patterns). However, constraint-based mining of models remains a quite open issue. Also, coupling schemes between the available database technology and inductive querying p- posals are not yet well understood. Finally, the de?nition of a general purpose inductive query language is still an on-going quest.


algorithms classification clustering constraint-based mining data management data mining database inductive databases knowledge discovery learning machine learning multi-objective regression pattern mining query languages query optimization

Editors and affiliations

  • Francesco Bonchi
    • 1
  • Jean-François Boulicaut
    • 2
  1. 1.Pisa KDD Laboratory, ISTI - C.N.R, Area della Ricerca di PisaPisaItaly
  2. 2.INSA-Lyon, LIRIS CNRS UMR5205VilleurbanneFrance

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