Nouvelle représentation concise exacte des motifs corrélés rares : Application à la détection d'intrusions

Abstract: Correlated rare pattern mining is an interesting issue in Data mining. In this respect, the set of correlated rare patterns w.r.t. to the bond correlation measure was studied in a recent work, in which the RCPR concise exact representation of the set of correlated rare patterns was proposed. However, none algorithm was proposed in order to mine this representation and none experiment was carried out to evaluate it. In this paper, we introduce the new RcprMiner algorithm allowing an efficient extraction of RCPR. We also present the IsRCP algorithm allowing the query of the RCPR representation in addition to the RCPRegeneration algorithm allowing the regeneration of the whole set RCP of rare correlated patterns starting from this representation. The carried out experiments highlight interesting compactness rates offered by RCPR. The effectiveness of the proposed classification method, based on generic rare correlated association rules derived from RCPR, has also been proved in the context of intrusion detection.