  Discovering the set of closed frequent patterns is one of the fundamental
problems in Data Mining. Recent Constraint Programming (CP) approaches for
declarative itemset mining have proven their usefulness and flexibility. But
the wide use of reified constraints in current CP approaches raises many
difficulties to cope with high dimensional datasets. This paper proposes CLOSED
PATTERN global constraint which does not require any reified constraints nor
any extra variables to encode efficiently the Closed Frequent Pattern Mining
(CFPM) constraint. CLOSED-PATTERN captures the particular semantics of the CFPM
problem in order to ensure a polynomial pruning algorithm ensuring domain
consistency. The computational properties of our constraint are analyzed and
their practical effectiveness is experimentally evaluated.
