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International Conference on Database and Expert Systems Applications

DEXA 2013: Database and Expert Systems Applications pp 109–123Cite as

Inferring Knowledge from Concise Representations of Both Frequent and Rare Jaccard Itemsets

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8056))

Abstract

Correlated pattern mining has become increasingly an important task in data mining and knowledge discovery. Recently, concise exact representations dedicated for frequent correlated and for rare correlated patterns according to the Jaccard measure were presented. In this paper, we offer a new method of inferring new knowledge from the introduced concise representations. A new generic approach, called Gmjp, allowing the extraction of the sets of frequent correlated patterns, of rare correlated patterns and their associated concise representations is introduced. Pieces of new knowledge in the form of associations rules can be either exact or approximate. We also illustrate the efficiency of our approach over several data sets and we prove that Jaccard-based classification rules have very encouraging results.

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Author information

Authors and Affiliations

  1. Computer Science Department, Faculty of Sciences of Tunis, LIPAH, Tunis, Tunisia

    Souad Bouasker & Sadok Ben Yahia

  2. Institut Telecom, Telecom SudParis, UMR 5157, CNRS Samovar, France

    Sadok Ben Yahia

Authors
  1. Souad Bouasker
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  2. Sadok Ben Yahia
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Editor information

Editors and Affiliations

  1. Instituto Tecnológico de Informática, Valencia, Spain

    Hendrik Decker

  2. Faculty of Electrical Engineering, Department of Cybernetics, Czech Technical University in Prague, 166 27, Prague 6, Czech Republic

    Lenka Lhotská

  3. Department of Computer Science, The University of Auckland, 1010, Auckland, New Zealand

    Sebastian Link

  4. Department of Information Technologies, University of Economics, Winston Churchill Square 4, 130 67, Prague 3, Czech Republic

    Josef Basl

  5. Institute of Software Technology, Vienna University of Technology, Favoritenstraße 9-11 / 188, 1040, Vienna, Austria

    A Min Tjoa

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© 2013 Springer-Verlag Berlin Heidelberg

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Bouasker, S., Ben Yahia, S. (2013). Inferring Knowledge from Concise Representations of Both Frequent and Rare Jaccard Itemsets. In: Decker, H., Lhotská, L., Link, S., Basl, J., Tjoa, A.M. (eds) Database and Expert Systems Applications. DEXA 2013. Lecture Notes in Computer Science, vol 8056. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40173-2_12

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  • DOI: https://doi.org/10.1007/978-3-642-40173-2_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40172-5

  • Online ISBN: 978-3-642-40173-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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