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Correlated pattern mining has increasingly become an important task in datamining since these patterns allow conveying knowledge about meaningful andsurprising relations among data. Frequent correlated patterns were thoroughlystudied in the literature. In this thesis, we propose to benefit from bothfrequent correlated as well as rare correlated patterns according to the bondcorrelation measure. We propose to extract a subset without information loss ofthe sets of frequent correlated and of rare correlated patterns, this subset iscalled ``Condensed Representation``. In this regard, we are based on thenotions derived from the Formal Concept Analysis FCA, specifically theequivalence classes associated to a closure operator fbond dedicated to thebond measure, to introduce new concise representations of both frequentcorrelated and rare correlated patterns.

Characterization and extraction of condensed representation of correlated patterns based on formal concept analysis