A Comparative Study of Local Search Algorithms for Correlation Clustering

This paper empirically compares four local search algorithms for correlation clustering by applying these to a variety of instances of the correlation clustering problem for the tasks of image segmentation, hand-written digit classification and social network analysis. Although the local search algorithms establish neither lower bounds nor approximation certificates, they converge monotonously to a fixpoint, offering a feasible solution at any time. For some algorithms, the time of convergence is affordable for all instances we consider. This finding encourages a broader application of correlation clustering, especially in settings where the number of clusters is not known and needs to be estimated from data.

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