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Recently, rare pattern mining proves to be of added-value in different datamining applications since these patterns allow conveying knowledge on rare andunexpected events. However, the extraction of rare patterns suffers from twomain limits, namely the large number of mined patterns in real-lifeapplications, as well as the low informativeness quality of several rarepatterns. In this situation, we propose to use the correlation measure, bond,in the mining process in order to only retain those rare patterns having acertain degree of correlation between their respective items. Acharacterization of the resulting set, of rare correlated patterns, is thencarried out based on the study of constraints of distinct types induced by therarity and the correlation. In addition, based on the equivalence classesassociated to a closure operator dedicated to the bond measure, we proposeconcise representations of rare correlated patterns. We then design a newalgorithm CRP_Miner dedicated to the extraction of the whole set of rarecorrelated patterns. We also introduce the CRPR_Miner algorithm allowing anefficient extraction of the proposed concise representations. In addition, wedesign two other algorithms which allow to us the query and the regeneration ofthe whole set of rare correlated patterns. The carried out experimental studiesshow the effectiveness of the algorithm CRPR_Miner and prove the compactnessrate offered by the proposed concise representations.

Motifs corr\'el\'es rares : Caract\'erisation et nouvelles repr\'esentations concises