Journal
OPTIMIZATION LETTERS
Volume 11, Issue 2, Pages 359-376Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s11590-015-0974-4
Keywords
Minimum sum-of-squares clustering; K-means; J-means; Heuristic; Variable neighborhood search
Funding
- RSF [14-41-00039]
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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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