Extracting Frequent Gradual Patterns Using Constraints Modeling

TL;DR

This paper introduces a SAT-based constraint modeling approach for discovering frequent gradual patterns in numerical datasets, leveraging modern SAT solvers for efficient enumeration and extension with additional constraints.

Contribution

It presents a novel declarative SAT-based method for gradual pattern mining, enabling flexible constraint integration and improved efficiency over traditional techniques.

Findings

Effective enumeration of gradual patterns demonstrated on real datasets

Flexible extension with temporal and other constraints possible

Shows practical feasibility of SAT-based approach in pattern mining

Abstract

In this paper, we propose a constraint-based modeling approach for the problem of discovering frequent gradual patterns in a numerical dataset. This SAT-based declarative approach offers an additional possibility to benefit from the recent progress in satisfiability testing and to exploit the efficiency of modern SAT solvers for enumerating all frequent gradual patterns in a numerical dataset. Our approach can easily be extended with extra constraints, such as temporal constraints in order to extract more specific patterns in a broad range of gradual patterns mining applications. We show the practical feasibility of our SAT model by running experiments on two real world datasets.