Prefix-Projection Global Constraint for Sequential Pattern Mining

TL;DR

This paper introduces a new global constraint for sequential pattern mining that improves efficiency and scalability, outperforming existing constraint programming methods and competing with ad hoc techniques on large datasets.

Contribution

A novel global constraint based on projected databases is proposed, enhancing the effectiveness of constraint programming in sequential pattern mining.

Findings

Outperforms existing CP approaches in efficiency

Competitively scales with ad hoc methods on large datasets

Demonstrates significant improvements in mining performance

Abstract

Sequential pattern mining under constraints is a challenging data mining task. Many efficient ad hoc methods have been developed for mining sequential patterns, but they are all suffering from a lack of genericity. Recent works have investigated Constraint Programming (CP) methods, but they are not still effective because of their encoding. In this paper, we propose a global constraint based on the projected databases principle which remedies to this drawback. Experiments show that our approach clearly outperforms CP approaches and competes well with ad hoc methods on large datasets.