A global constraint for closed itemset mining

TL;DR

This paper introduces a new global constraint for closed itemset mining that simplifies the encoding process, improves computational efficiency, and ensures domain consistency without reified constraints, demonstrated through experimental evaluation.

Contribution

It proposes the CLOSED-PATTERN global constraint that efficiently encodes closed pattern mining without reified constraints, enhancing performance and simplicity.

Findings

Polynomial pruning algorithm guarantees domain consistency.

Experimental results show improved efficiency over existing methods.

No need for reified constraints or extra variables in encoding.

Abstract

Discovering the set of closed frequent patterns is one of the fundamental problems in Data Mining. Recent Constraint Programming (CP) approaches for declarative itemset mining have proven their usefulness and flexibility. But the wide use of reified constraints in current CP approaches raises many difficulties to cope with high dimensional datasets. This paper proposes CLOSED PATTERN global constraint which does not require any reified constraints nor any extra variables to encode efficiently the Closed Frequent Pattern Mining (CFPM) constraint. CLOSED-PATTERN captures the particular semantics of the CFPM problem in order to ensure a polynomial pruning algorithm ensuring domain consistency. The computational properties of our constraint are analyzed and their practical effectiveness is experimentally evaluated.