Affiliations: [a] Faculty of Sciences of Tunis, University of Tunis ElManar, LIPAH-LR 11ES14, Tunis, Tunisia | [b] Institut Telecom, Telecom SudParis, UMR 5157 CNRS, Samovar, France
Correspondence:
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Corresponding author: Sadok Ben Yahia, Faculty of Sciences of Tunis, University of Tunis El Manar, LIPAH, 2092, Tunis, Tunisia. Tel.: +216 71 872 200; Fax: +216 71 885 190; E-mail: sadok.benyahia@fst.rnu.tn.
Note: [1] This paper is a largely extended version of our work proposed in [8].
Abstract: During the last years, many works focused on the exploitation and the extraction of rare patterns. In fact, these patterns allow conveying knowledge on rare and unexpected events. They are hence useful in several application fields. Nevertheless, a main moan addressed to rare pattern extraction approaches is, on the one hand, their high number and, on the other hand, the low quality of several mined patterns. The latter can indeed not present strong correlations between the items they contain. In order to overcome these limits, we propose to integrate the correlation measure bond aiming at only mining the set of rare patterns fulfilling this measure. A characterization of the resulting set, of rare correlated patterns, is then carried out based on the study of constraints of distinct types induced by the rarity and the correlation. In addition, based on the equivalence classes associated to a closure operator dedicated to the bond measure, we introduce new concise representations of rare correlated patterns as well as the derivation process of the generic bases of the rare correlated association rules. We then design the RcprMiner algorithm allowing an efficient extraction of the proposed concise representations. Carried out experimental studies highlight the very encouraging compactness rates offered by the proposed concise representations and show the good performance of the RcprMiner algorithm.
