Towards more efficient local search algorithms for constrained clustering

• Published in: Information sciences Vol. 621; pp. 287 - 307
• Main Authors: Gao, Jian, Tao, Xiaoxia, Cai, Shaowei
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.04.2023
• Subjects: Constrained clustering; Constraint optimization problem; Local search; Search strategy; Local search; Constraint optimization problem; Search strategy; Constrained clustering
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2022.11.107

•The constrained clustering problem is studied.•An efficient local search algorithm is proposed.•A node filtering strategy is introduced for improving efficiency.•The proposed algorithm is more effective than state-of-the-art heuristics. Constrained clustering extends clustering by integrating user constraints, and aims to determine an optimal assignment under the constraints. In this paper, we propose a local search algorithm called FastCCP to solve the constrained clustering problem. In the algorithm, instances connected by must-link constraints are first merged into nodes, and then, a local search method is performed to handle the cannot-link constraints while minimizing the Within-Cluster Sum of Squares (WCSS). Several strategies are proposed to enhance the solution diversity and achieve a trade-off between constraint satisfaction and WCSS minimization during the search. Furthermore, a node-filtering strategy is proposed to improve the efficiency of the algorithm. Experiments are performed on benchmark datasets to evaluate our algorithm. The comparative results indicate that our algorithm outperforms state-of-the-art algorithms in terms of both the solution quality and CPU runtime.