Evidential data mining: precise support and confidence

Ahmed Samet (1) , Eric Lefevre (2) , Sadok Ben Yahia (3)

1 ICube - Laboratoire des sciences de l'ingénieur, de l'informatique et de l'imagerie
2 LGI2A - Laboratoire de Génie Informatique et d'Automatique de l'Artois
3 UTM - Université de Tunis El Manar

Associative classification has been shown to provide interesting results whenever of use to classify data. With the increasing complexity of new databases, retrieving valuable information and classifying incoming data is becoming a thriving and compelling issue. The evidential database is a new type of database that represents imprecision and uncertainty. In this respect, extracting pertinent information such as frequent patterns and association rules is of paramount importance task. In this work, we tackle the problem of pertinent information extraction from an evidential database. A new data mining approach, denoted EDMA, is introduced that extracts frequent patterns overcoming the limits of pioneering works of the literature. A new classifier based on evidential association rules is thus introduced. The obtained association rules, as well as their respective confidence values, are studied and weighted with respect to their relevance. The proposed methods are thoroughly experimented on several synthetic evidential databases and showed performance improvement.

Mots clés: Evidential database; Evidential confidence; Evidential support; Associative classification

Domaines: Base de données [cs.DB]

Ahmed Samet, Eric Lefevre, Sadok Ben Yahia. Evidential data mining: precise support and confidence. Journal of Intelligent Information Systems, 2016, 47 (1), pp.135-163. ⟨10.1007/s10844-016-0396-5⟩. ⟨hal-03354130⟩