J-means and I-means for minimum sum-of-squares clustering on networks |
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Authors: | Alexey Nikolaev Nenad Mladenović Raca Todosijević |
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Institution: | 1.Laboratory of Algorithms and Technologies for Networks Analysis,National Research University Higher School of Economics,Nizhny Novgorod,Russia;2.LAMIH, Université de Valenciennes et du Hainaut-Cambresis,Valenciennes Cedex 9,France;3.Mathematical Institute, Serbian Academy of Science and Arts,Belgrade,Serbia |
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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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