Constraint-Based Search of Different Kinds of Discriminative Patterns

The state-of-the-art Data-Peeler algorithm extracts closed patterns in n-ary relations. Because it refines both a lower and an upper bound of the pattern space, Data-Peeler can, in some circumstances, guarantee that a region of that space does not contain any closed n-set satisfying some relevance constraint. Whenever it happens, such region is not explored and some time is saved. This paper shows that some constraints, which Data-Peeler can efficiently enforce, define useful patterns in the context of a relation with groups of elements in arbitrary dimensions. For instance, it can list the so-called straddling biclusters, which cover at least some given portions of every group. It can discover, as well, closed n-sets that discriminate a group from the others. The experimental section focuses on the latter case. It shows that Data-Peeler is highly competitive despite its general enumeration principles and its expressive class of constraints that open up new applicative perspectives.