A global Constraint for mining Sequential Patterns with GAP constraint

TL;DR

This paper introduces the GAP-SEQ global constraint for sequential pattern mining that effectively handles gap constraints, outperforming existing methods in efficiency on large datasets.

Contribution

The paper presents a novel global constraint GAP-SEQ for SPM with gap constraints, extending previous work and improving efficiency over existing approaches.

Findings

GAP-SEQ outperforms CP approaches and cSpade on large datasets.

The method effectively handles SPM with or without gap constraints.

Experiments demonstrate significant efficiency improvements.

Abstract

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.