  Constraint Programming is a powerful paradigm to model and solve
combinatorial problems. While there are many kinds of constraints, the table
constraint is perhaps the most significant-being the most well-studied and has
the ability to encode any other constraints defined on finite variables.
However, constraints can be very voluminous and their size can grow
exponentially with their arity. To reduce space and the time complexity,
researchers have focused on various forms of compression. In this paper we
propose a new approach based on maximal frequent itemsets technique and area
measure for enumerating the maximal frequent itemsets relevant for compressing
table constraints. Our experimental results show the effectiveness and
efficiency of this approach on compression and on solving compressed table
constraints.
