A Global Constraint for Mining Sequential Patterns with GAP Constraint
Sequential pattern mining (SPM) under gap constraint is a challenging task. Many efficient specialized methods have been developed but they are all suffering from a lack of genericity. The Constraint Programming (CP) approaches are not so effective because of the size of their encodings. In [7] we have proposed the global constraint Prefix Projection for SPM which remedies to this drawback. However, this global constraint cannot be directly extended to support gap constraint. In this paper, we propose the global constraint GAP SEQ enabling to handle SPM with or without gap constraint. GAP SEQ relies on the principle of right pattern extensions. Experiments show that our approach clearly outperforms both CP approaches and the state of the art cSpade method on large datasets.
CPAIOR