Authors: Nikolaev, Alexey
Mladenović, Nenad 
Todosijević, Raca 
Affiliations: Mathematical Institute of the Serbian Academy of Sciences and Arts 
Title: J-means and I-means for minimum sum-of-squares clustering on networks
Journal: Optimization Letters
Volume: 11
Issue: 2
First page: 359
Last page: 376
Issue Date: 1-Feb-2017
Rank: M21
ISSN: 1862-4472
DOI: 10.1007/s11590-015-0974-4
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.
Keywords: Heuristic | J-means | K-means | Minimum sum-of-squares clustering | Variable neighborhood search
Publisher: Springer Link
Project: RSF, Grant 14-41-00039

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