|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||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.
|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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