Algorithmes d'extraction et d'interrogation d'une représentation concise exacte des motifs corrélés rares : application à la détection d'intrusions

National audienceIn this paper, we introduce the algorithm RCPRMINER allowing the extraction of RCPR. We also present dedicated algorithms allowing the query of the RCPR representation and the regeneration of the whole set RCP starting from this representation. 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.Nous proposons, dans ce papier, l'algorithme RCPRMINER d'extraction de la représentation RMCR de l'ensemble MCR des motifs corrélés rares. Les algorithmes d'interrogation de cette représentation et de régénération de l'ensemble MCR à partir de RMCR sont aussi introduits. En outre, nous décrivons le processus de classification basée sur les règles génériques corrélées rares et son application dans la détection d'intrusion

Pregnancy Associated Breast Cancer Gene Expressions : New Insights on Their Regulation Based on Rare Correlated Patterns

Breast-cancer (BC) is the most common invasive cancer in women, with considerable death. Given that, BC is classified as a hormone-dependent cancer, when it collides with pregnancy, different questions may arise for which there are still no convincing answers. To deal with this issue, two new frameworks are proposed within this paper: CoRaM and Dist-CoRaM. The former is the first unified framework dedicated to the extraction of a generic basis of Correlated-Rare Association rules from gene expression data. The proposed approach has been successfully applied on a breast-cancer Gene Expression Matrix (GSE1379) with very promising results. The latter, the Dist-CoRaM approach, is a big-data processing based on Apache spark framework, dealing with correlation mining from micro-array pregnancy associated breast-cancer assays (PABC) data. It is successfully applied on the (GSE31192) gene expression matrix (GEM). The correlated patterns of gene-sets shed light on the fact that PABC exhibits heightened aggressiveness compared to cancers for Non-PABC women. Our findings suggest that higher levels of estrogen and progesterone hormones, unfortunately, are very keen to the increase of the tumor aggressiveness and the proliferation of the cancer