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Efficient Data Mining Based on Formal Concept Analysis

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Database and Expert Systems Applications (DEXA 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2453))

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Abstract

Formal Concept Analysis is an unsupervised learning technique for conceptual clustering. We introduce the notion of iceberg concept lattices and show their use in Knowledge Discovery in Databases (KDD). Iceberg lattices are designed for analyzing very large databases. In particular they serve as a condensed representation of frequent patterns as known from association rule mining.

In order to show the interplay between Formal Concept Analysis and association rule mining, we discuss the algorithm Titanic. We show that iceberg concept lattices are a starting point for computing condensed sets of association rules without loss of information, and are a visualization method for the resulting rules.

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Author information

Authors and Affiliations

  1. Institut für Angewandte Informatik und Formale Beschreibungsverfahren AIFB, Universität Karlsruhe, D-76128, Karlsruhe, Germany

    Gerd Stumme

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  1. Gerd Stumme
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Editors and Affiliations

  1. Université Paul Sabatier, IRIT, 118 route de Narbonne, 31062, Toulouse Cedex, France

    Abdelkader Hameurlain

  2. Département Informatique, Université Aix-Marseille II, IUT, 413 Avenue Gaston Berger, 13625, Aix-en-Provence Cedex 1, France

    Rosine Cicchetti

  3. Institute of Applied Computer Science, University of Linz, Altenbergerstr. 69, 4040, Linz, Austria

    Roland Traunmüller

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Stumme, G. (2002). Efficient Data Mining Based on Formal Concept Analysis. In: Hameurlain, A., Cicchetti, R., Traunmüller, R. (eds) Database and Expert Systems Applications. DEXA 2002. Lecture Notes in Computer Science, vol 2453. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46146-9_53

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  • DOI: https://doi.org/10.1007/3-540-46146-9_53

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