Extracting Frequent Gradual Patterns Using Constraints Modeling
This work addresses the challenge of pattern mining for data analysts, but appears incremental as it adapts existing SAT solvers to a specific domain.
The authors tackled the problem of discovering frequent gradual patterns in numerical datasets by proposing a constraint-based modeling approach, and demonstrated its practical feasibility through experiments on two real-world datasets.
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.