Concept Stability as a Tool for Pattern Selection

Aleksey Buzmakov 1, 2 Sergei O. Kuznetsov 2 Amedeo Napoli 1
1 ORPAILLEUR - Knowledge representation, reasonning
Inria Nancy - Grand Est, LORIA - NLPKD - Department of Natural Language Processing & Knowledge Discovery
Abstract : Data mining aims at finding interesting patterns from datasets, where ``interesting'' means reflecting intrinsic dependencies in the domain of interest rather than just in the dataset. Concept stability is a popular relevancy measure in FCA but its behaviour have never been studied on various datasets. In this paper we propose an approach to study this behaviour. Our approach is based on a comparison of stability computation on datasets produced by the same general population. Experimental results of this paper show that high stability of a concept in one dataset suggests that concepts with the same intent in other dataset drawn from the population have also high stability. Moreover, experiments shows some asymptotic behaviour of stability in such kind of experiments when dataset size increases.
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Communication dans un congrès
FCA4AI 2014. What can FCA do for Artificial Intelligence?, Aug 2014, Praque, Czech Republic
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Aleksey Buzmakov, Sergei O. Kuznetsov, Amedeo Napoli. Concept Stability as a Tool for Pattern Selection. FCA4AI 2014. What can FCA do for Artificial Intelligence?, Aug 2014, Praque, Czech Republic. 〈hal-01095903〉

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