Improving constrained clustering via swarm intelligence

Abstract By simulating the clustering behavior of the real-world ant colonies, we propose in this paper a constrained ant clustering algorithm. This algorithm is embedded with the heuristic walk mechanism based on random walk to deal with the constrained clustering problems given pairwise must-link and cannot-link constraints. Experimental results show that our approach is more effective on both the synthetic datasets and the real datasets compared with the Cop-Kmeans and ant-based clustering algorithm.

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