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 a region is unexplored and computation 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, which are the focus of the experimental section. 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.