  The problem of discovering frequent itemsets including rare ones has received
a great deal of attention. The mining process needs to be flexible enough to
extract frequent and rare regularities at once. On the other hand, it has
recently been shown that constraint programming is a flexible way to tackle
data mining tasks. In this paper, we propose a constraint programming approach
for mining itemsets with multiple minimum supports. Our approach provides the
user with the possibility to express any kind of constraints on the minimum
item supports. An experimental analysis shows the practical effectiveness of
our approach compared to the state of the art.
