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Extraction et sélection de motifs émergents minimaux : application à la chémoinformatique

Mouhamadou Bamba Kane 1
1 Equipe CODAG - Laboratoire GREYC - UMR6072
GREYC - Groupe de Recherche en Informatique, Image, Automatique et Instrumentation de Caen
Abstract : Pattern discovery is an important field of Knowledge Discovery in Databases.This work deals with the extraction of minimal emerging patterns. We propose a new efficientmethod which allows to extract the minimal emerging patterns with or without constraint ofsupport ; unlike existing methods that typically extract the most supported minimal emergentpatterns, at the risk of missing interesting but less supported patterns. Moreover, our methodtakes into account the absence of attribute that brings a new interesting knowledge.Considering the rules associated with emerging patterns highly supported as prototype rules,we have experimentally shown that this set of rules has good confidence on the covered objectsbut unfortunately does not cover a significant part of the objects ; which is a disavadntagefor their use in classification. We propose a prototype-based selection method that improvesthe coverage of the set of the prototype rules without a significative loss on their confidence.We apply our prototype-based selection method to a chemical data relating to the aquaticenvironment : Aquatox. In a classification context, it allows chemists to better explain theclassification of molecules, which, without this method of selection, would be predicted by theuse of a default rule.
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Mouhamadou Bamba Kane. Extraction et sélection de motifs émergents minimaux : application à la chémoinformatique. Technologies Émergeantes [cs.ET]. Normandie Université, 2017. Français. ⟨NNT : 2017NORMC223⟩. ⟨tel-01665320⟩

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