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J-means and I-means for minimum sum-of-squares clustering on networks

Abstract : Given a graph, the Edge minimum sum-of-squares clustering problem requires finding p prototypes (cluster centres) by minimizing the sum of their squared distances from a set of vertices to their nearest prototype, where a prototype can be either a vertex or an inner point of an edge. In this paper we have implemented Variable neighborhood search based heuristic for solving it. We consider three different local search procedures, K-means, J-means, and a new I-means heuristic. Experimental results indicate that the implemented VNS-based heuristic produces the best known results in the literature.
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https://hal-uphf.archives-ouvertes.fr/hal-03402127
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Submitted on : Monday, October 25, 2021 - 3:22:17 PM
Last modification on : Wednesday, November 3, 2021 - 5:24:18 AM

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Alexey Nikolaev, Nenad Mladenovic, Raca Todosijević. J-means and I-means for minimum sum-of-squares clustering on networks. Optimization Letters, Springer Verlag, 2017, 11 (2), pp.359-376. ⟨10.1007/s11590-015-0974-4⟩. ⟨hal-03402127⟩

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