A Local Search for a Graph Correlation Clustering

In the clustering problems one has to partition a given set of objects into some subsets (called clusters) taking into consideration only similarity of the objects. We consider a version of the clustering problem when the number of clusters does not exceed a positive integer k and the goal is to minimize the number of edges between clusters and the number of missing edges within clusters. This problem is NP-hard for any k ≥ 2. We propose a polynomial time k-approximation algorithm for this problem.

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