Citation: Proceedings of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 11, pages 403–406 (2017)
Abstract. Correlation clustering is a NP-hard problem, and for large graphs finding even just a good approximation of the optimal solution is a hard task. In previous articles we have suggested a contraction method and its divide and conquer variant. In this article we present several improvements of this method (preprocessing, quasi-parallelism, etc.) and prepare it for parallelism. Based on speed tests we show where it helps the concurrent execution, and where it pulls us back.
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