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Efficient Mining of Association Rules Based on Formal Concept Analysis

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Formal Concept Analysis

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3626))

Abstract

Association rules are a popular knowledge discovery technique for warehouse basket analysis. They indicate which items of the warehouse are frequently bought together. The problem of association rule mining has first been stated in 1993. Five years later, several research groups discovered that this problem has a strong connection to Formal Concept Analysis (FCA). In this survey, we will first introduce some basic ideas of this connection along a specific algorithm, Titanic, and show how FCA helps in reducing the number of resulting rules without loss of information, before giving a general overview over the history and state of the art of applying FCA for association rule mining.

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Author information

Authors and Affiliations

  1. Département d’Informatique, IUT d’Aix-en-Provence, Avenue Gaston Berger, F-13625, Aix-en-Provence cedex, France

    Lotfi Lakhal

  2. Chair of Knowledge & Data Engineering, Department of Mathematics and Computer Science, University of Kassel, Wilhelmshöher Allee 73, D–34121, Kassel, Germany

    Gerd Stumme

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  1. Lotfi Lakhal
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  2. Gerd Stumme
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Editors and Affiliations

  1. Institute of Algebra, University of Technology, Dresden, Germany

    Bernhard Ganter

  2. Research Center L3S, Appelstr. 9a, D-30167, Hannover, Germany

    Gerd Stumme

  3. Fachbereich Mathematik, Technische Universität Darmstadt, D–64289, Darmstadt

    Rudolf Wille

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Lakhal, L., Stumme, G. (2005). Efficient Mining of Association Rules Based on Formal Concept Analysis. In: Ganter, B., Stumme, G., Wille, R. (eds) Formal Concept Analysis. Lecture Notes in Computer Science(), vol 3626. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11528784_10

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  • DOI: https://doi.org/10.1007/11528784_10

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27891-7

  • Online ISBN: 978-3-540-31881-1

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