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An Efficient Method for Mining Informative Association Rules in Knowledge Extraction

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Machine Learning and Knowledge Extraction (CD-MAKE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12279))

Abstract

Mining association rules is an important problem in Knowledge Extraction (KE). This paper proposes an efficient method for mining simultaneously informative positive and negative association rules, using a new selective pair support-\(M_{GK}\). For this, we define four new bases of positive and negative association rules, based on Galois connection semantics. These bases are characterized by frequent closed itemsets, maximal frequent itemsets, and their generator itemsets; it consists of the non-redundant exact and approximate association rules having minimal premise and maximal conclusion, i.e. the informative association rules. We introduce Nonred algorithm allowing to generate these bases and all valid informative association rules. Results experiments carried out on reference datasets show the usefulness of this approach.

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Notes

  1. 1.

    An association of the form \(X\rightarrow \overline{Y}\), \(\overline{X}\rightarrow Y\) and \(\overline{X}\rightarrow \overline{Y}\), where \(\overline{I}=\lnot I={\mathcal I}\backslash I\).

  2. 2.

    http://www.almaden.ibm.com/cs/quest/syndata.html.

  3. 3.

    http://kdd.ics.uci.edu/.

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Authors and Affiliations

  1. Laboratoire de Mathématiques et Informatique ENSET, Université d’Antsiranana, Antsiranana, Madagascar

    Parfait Bemarisika & André Totohasina

Authors
  1. Parfait Bemarisika
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  2. André Totohasina
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Corresponding author

Correspondence to Parfait Bemarisika .

Editor information

Editors and Affiliations

  1. Human-Centered AI Lab, Institute for Medical Informatics, Statistics and Doumentation, Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. UAS St. Pölten, St. Pölten, Austria

    Peter Kieseberg

  3. Institute of Software Technology and Interactive Systems, Technical University of Vienna, Vienna, Austria

    A Min Tjoa

  4. SBA Research, Vienna, Austria

    Edgar Weippl

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Bemarisika, P., Totohasina, A. (2020). An Efficient Method for Mining Informative Association Rules in Knowledge Extraction. In: Holzinger, A., Kieseberg, P., Tjoa, A., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2020. Lecture Notes in Computer Science(), vol 12279. Springer, Cham. https://doi.org/10.1007/978-3-030-57321-8_13

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  • DOI: https://doi.org/10.1007/978-3-030-57321-8_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-57320-1

  • Online ISBN: 978-3-030-57321-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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