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

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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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Acknowledgments

The work was conducted at National Research University Higher School of Economics and supported by RSF Grant 14-41-00039.

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Authors and Affiliations

  1. Laboratory of Algorithms and Technologies for Networks Analysis, National Research University Higher School of Economics, 136 Rodionova street, Nizhny Novgorod, 603093, Russia

    Alexey Nikolaev

  2. LAMIH, Université de Valenciennes et du Hainaut-Cambresis, Campus Mont Houy, 59313, Valenciennes Cedex 9, France

    Nenad Mladenović & Raca Todosijević

  3. Mathematical Institute, Serbian Academy of Science and Arts, 11000, Belgrade, Serbia

    Nenad Mladenović & Raca Todosijević

Authors
  1. Alexey Nikolaev
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  2. Nenad Mladenović
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  3. Raca Todosijević
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Correspondence to Alexey Nikolaev.

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Nikolaev, A., Mladenović, N. & Todosijević, R. J-means and I-means for minimum sum-of-squares clustering on networks. Optim Lett 11, 359–376 (2017). https://doi.org/10.1007/s11590-015-0974-4

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  • DOI: https://doi.org/10.1007/s11590-015-0974-4

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