Abstract
This paper addresses the Cluster Editing problem. The objective of this problem is to transform a graph into a disjoint union of cliques using a minimum number of edge modifications. This problem has been considered in the context of bioinformatics, document clustering, image segmentation, consensus clustering, qualitative data clustering among others. Here, we focus on the Integer Linear Programming (ILP) formulation of this problem. The ILP creates models with a large number of constraints. This limits the size of the problems that can be optimally solved. In order to overcome this limitation, this paper proposes a novel preprocessing technique to construct a reduced model that feasibly maintains the optimal solution set. In comparison to the original model, the reduced model preserves the optimal solution and achieves considerable computational time speed-up in the experiments performed on different datasets.
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Acknowledgements
The authors thanks FAPESP (Grant No. 2011/18496-7), CNPq (Grant No. 310908/2015-9 and 301836/2014-0), CAPES and IBM for support.
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Lorena, L.H.N., Quiles, M.G., de Carvalho, A.C.P.d.L.F., Lorena, L.A.N. (2018). Preprocessing Technique for Cluster Editing via Integer Linear Programming. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_27
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