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Article Dans Une Revue Annals of Mathematics and Artificial Intelligence Année : 2014

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

Radim Belohlavek
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  • PersonId : 1044619
Dhouha Grissa
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  • PersonId : 771971
  • IdRef : 179415352
Jan Outrata
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  • PersonId : 1044620

Résumé

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 R. Belohlavek et al. 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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Dates et versions

hal-02079269 , version 1 (10-04-2019)

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Radim Belohlavek, Dhouha Grissa, Sylvie Guillaume, Engelbert Mephu Nguifo, Jan Outrata. Boolean factors as a means of clustering of interestingness measures of association rules. Annals of Mathematics and Artificial Intelligence, 2014, 70 (1-2), pp.151-184. ⟨10.1007/s10472-013-9370-x⟩. ⟨hal-02079269⟩
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