J-means and I-means for minimum sum-of-squares clustering on networks - Université Polytechnique des Hauts-de-France Access content directly
Journal Articles Optimization Letters Year : 2017

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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Dates and versions

hal-03402127 , version 1 (25-10-2021)

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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, 2017, 11 (2), pp.359-376. ⟨10.1007/s11590-015-0974-4⟩. ⟨hal-03402127⟩
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