  Correlated pattern mining has increasingly become an important task in data
mining since these patterns allow conveying knowledge about meaningful and
surprising relations among data. Frequent correlated patterns were thoroughly
studied in the literature. In this thesis, we propose to benefit from both
frequent correlated as well as rare correlated patterns according to the bond
correlation measure. We propose to extract a subset without information loss of
the sets of frequent correlated and of rare correlated patterns, this subset is
called ``Condensed Representation``. In this regard, we are based on the
notions derived from the Formal Concept Analysis FCA, specifically the
equivalence classes associated to a closure operator fbond dedicated to the
bond measure, to introduce new concise representations of both frequent
correlated and rare correlated patterns.
