Towards more efficient local search algorithms for constrained clustering
•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...
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| Published in | Information sciences Vol. 621; pp. 287 - 307 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Inc
01.04.2023
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| Online Access | Get full text |
| ISSN | 0020-0255 1872-6291 |
| DOI | 10.1016/j.ins.2022.11.107 |
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| Abstract | •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. |
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| AbstractList | •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. |
| Author | Tao, Xiaoxia Gao, Jian Cai, Shaowei |
| Author_xml | – sequence: 1 givenname: Jian surname: Gao fullname: Gao, Jian organization: College of Information Science and Technology, Northeast Normal University, Changchun, China – sequence: 2 givenname: Xiaoxia surname: Tao fullname: Tao, Xiaoxia organization: School of Information Science and Technology, Dalian University of Science and Technology, Dalian, China – sequence: 3 givenname: Shaowei surname: Cai fullname: Cai, Shaowei email: caisw@ios.ac.cn organization: State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China |
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