  The hardness of finite domain Constraint Satisfaction Problems (CSPs) is a
very important research area in Constraint Programming (CP) community. However,
this problem has not yet attracted much attention from the researchers in the
association rule mining community. As a popular data mining technique,
association rule mining has an extremely wide application area and it has
already been successfully applied to many interdisciplines. In this paper, we
study the association rule mining techniques and propose a cascaded approach to
extract the interesting patterns of the hard CSPs. As far as we know, this
problem is investigated with the data mining techniques for the first time.
Specifically, we generate the random CSPs and collect their characteristics by
solving all the CSP instances, and then apply the data mining techniques on the
data set and further to discover the interesting patterns of the hardness of
the randomly generated CSPs
