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Regular version of the site

Article

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

Optimization Letters. 2017. Vol. 11. No. 2. P. 359-376.
Nikolaev A., Mladenovic N., Todosijevic R.

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.