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Boolean factors as a means of clustering of interestingness measures of association rules

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Abstract

Measures of interestingness play a crucial role in association rule mining. An important methodological problem, on which several papers appeared in the literature, is to provide a reasonable classification of the measures. In this paper, we explore Boolean factor analysis, which uses formal concepts corresponding to classes of measures as factors, for the purpose of clustering of the measures. Unlike the existing studies, our method reveals overlapping clusters of interestingness measures. We argue that the overlap between clusters is a desired feature of natural groupings of measures and that because formal concepts are used as factors in Boolean factor analysis, the resulting clusters have a clear meaning and are easy to interpret. We conduct three case studies on clustering of measures, provide interpretations of the resulting clusters and compare the results to those of the previous approaches reported in the literature.

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Authors and Affiliations

  1. Data Analysis and Modeling Lab, Dept. Computer Science, Palacky University Olomouc, 17. listopadu 12, 771 46, Olomouc, Czech Republic

    Radim Belohlavek & Jan Outrata

  2. LIMOS, Clermont Université, Université Blaise Pascal, BP 10448, 63000, Clermont-Ferrand, France

    Dhouha Grissa & Engelbert Mephu Nguifo

  3. LIMOS, Clermont Université, Université d’Auvergne, BP 10448, 63000, Clermont-Ferrand, France

    Sylvie Guillaume

  4. LIMOS, CNRS, UMR, 6158, 63173, Aubiére, France

    Dhouha Grissa, Sylvie Guillaume & Engelbert Mephu Nguifo

  5. LIPAH, Département d’Informatique, Faculté des Sciences de Tunis, Campus Universitaire, 1060, Tunis, Tunisie

    Dhouha Grissa

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  1. Radim Belohlavek
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  2. Dhouha Grissa
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  3. Sylvie Guillaume
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  4. Engelbert Mephu Nguifo
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  5. Jan Outrata
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Correspondence to Jan Outrata.

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Belohlavek, R., Grissa, D., Guillaume, S. et al. Boolean factors as a means of clustering of interestingness measures of association rules. Ann Math Artif Intell 70, 151–184 (2014). https://doi.org/10.1007/s10472-013-9370-x

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Keywords

Mathematics Subject Classifications (2010)

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