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Knowledge Discovery in Inductive Databases

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

  • Conference proceedings
  • © 2006

Overview

Editors:
  1. Francesco Bonchi

    1. Pisa KDD Laboratory, ISTI - C.N.R, Area della Ricerca di Pisa, Pisa, Italy

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  2. Jean-François Boulicaut

    1. INSA-Lyon, LIRIS CNRS UMR5205, Villeurbanne, France

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

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Included in the following conference series:

Conference proceedings info: KDID 2005.

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Table of contents (14 papers)

  1. Front Matter

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  2. Invited Papers

    1. Data Mining in Inductive Databases

      • Arno Siebes
      Pages 1-23

    2. Mining Databases and Data Streams with Query Languages and Rules

      • Carlo Zaniolo
      Pages 24-37

  3. Contributed Papers

    1. Memory-Aware Frequent k-Itemset Mining

      • Maurizio Atzori, Paolo Mancarella, Franco Turini
      Pages 38-54

    2. Constraint-Based Mining of Fault-Tolerant Patterns from Boolean Data

      • Jérémy Besson, Ruggero G. Pensa, Céline Robardet, Jean-François Boulicaut
      Pages 55-71

    3. Experiment Databases: A Novel Methodology for Experimental Research

      • Hendrik Blockeel
      Pages 72-85

    4. Quick Inclusion-Exclusion

      • Toon Calders, Bart Goethals
      Pages 86-103

    5. Towards Mining Frequent Queries in Star Schemes

      • Tao-Yuan Jen, Dominique Laurent, Nicolas Spyratos, Oumar Sy
      Pages 104-123

    6. Inductive Databases in the Relational Model: The Data as the Bridge

      • Stefan Kramer, Volker Aufschild, Andreas Hapfelmeier, Alexander Jarasch, Kristina Kessler, Stefan Reckow et al.
      Pages 124-138

    7. Transaction Databases, Frequent Itemsets, and Their Condensed Representations

      • Taneli Mielikäinen
      Pages 139-164

    8. Multi-class Correlated Pattern Mining

      • Siegfried Nijssen, Joost N. Kok
      Pages 165-187

    9. Shaping SQL-Based Frequent Pattern Mining Algorithms

      • Csaba István Sidló, András Lukács
      Pages 188-201

    10. Exploiting Virtual Patterns for Automatically Pruning the Search Space

      • Arnaud Soulet, Bruno Crémilleux
      Pages 202-221

    11. Constraint Based Induction of Multi-objective Regression Trees

      • Jan Struyf, Sašo Džeroski
      Pages 222-233

    12. Learning Predictive Clustering Rules

      • Bernard Ženko, Sašo Džeroski, Jan Struyf
      Pages 234-250

  4. Back Matter

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  1. Knowledge Discovery in Inductive Databases

Keywords

About this book

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.

Editors and Affiliations

  • Pisa KDD Laboratory, ISTI - C.N.R, Area della Ricerca di Pisa, Pisa, Italy

    Francesco Bonchi

  • INSA-Lyon, LIRIS CNRS UMR5205, Villeurbanne, France

    Jean-François Boulicaut

Bibliographic Information

  • Book Title: Knowledge Discovery in Inductive Databases

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

  • Editors: Francesco Bonchi, Jean-François Boulicaut

  • Series Title: Lecture Notes in Computer Science

  • DOI: https://doi.org/10.1007/11733492

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Computer Science, Computer Science (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2006

  • Softcover ISBN: 978-3-540-33292-3Published: 31 March 2006

  • eBook ISBN: 978-3-540-33293-0Published: 05 March 2006

  • Series ISSN: 0302-9743

  • Series E-ISSN: 1611-3349

  • Edition Number: 1

  • Number of Pages: VIII, 252

  • Topics: Data Structures and Information Theory, Database Management, Artificial Intelligence

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