A novel approach to metaheuristic-driven threshold optimization in frequent high-order pattern mining

Frequent pattern mining is a fundamental task in data mining, although its stability and effectiveness are generally constrained by the sensitivity of the frequency threshold. Simplicial complexes capture higher-order relationships by representing multi-entity interactions beyond simple pairwise connections. When data are represented as simplicial complexes, the combinatorial explosion in the search space further intensifies computational challenges. This study proposes, FreSCo[Formula: see text], an enhanced FreSCo framework that incorporates an automatic threshold optimization mechanism driven by Particle Swarm Optimization (PSO). The key objective is to dynamically balance pattern coverage and processing overhead. The proposed framework features adaptive threshold selection, early pruning strategies, caching-based reuse to expedite convergence, and reduce redundant computations. Experimental evaluations on three benchmark datasets, including ETFs, Zebra, and Enron, demonstrate significant performance improvements with average runtime reductions of 37.19% on ETFs, 15.55% on Zebra, and 15.39% on Enron, while maintaining accuracy and stability across 10 independent trials. These results indicate that the PSO-optimized FreSCo framework achieves faster convergence, improved scalability, and increased efficiency in frequent simplicial complex mining. Consequently, this framework establishes a robust foundation for metaheuristic-driven optimization approaches in higher-order data mining.