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dc.contributor.authorNikolaev, Alexeyen
dc.contributor.authorMladenović, Nenaden
dc.contributor.authorTodosijević, Racaen
dc.date.accessioned2020-05-02T16:41:57Z-
dc.date.available2020-05-02T16:41:57Z-
dc.date.issued2017-02-01en
dc.identifier.issn1862-4472en
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/2414-
dc.description.abstractGiven 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.en
dc.publisherSpringer Link-
dc.relationRSF, Grant 14-41-00039-
dc.relation.ispartofOptimization Lettersen
dc.subjectHeuristic | J-means | K-means | Minimum sum-of-squares clustering | Variable neighborhood searchen
dc.titleJ-means and I-means for minimum sum-of-squares clustering on networksen
dc.typeArticleen
dc.identifier.doi10.1007/s11590-015-0974-4en
dc.identifier.scopus2-s2.0-84949672620en
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Arts-
dc.relation.firstpage359en
dc.relation.lastpage376en
dc.relation.issue2en
dc.relation.volume11en
dc.description.rankM21-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextnone-
item.fulltextNo Fulltext-
crisitem.author.orcid0000-0001-6655-0409-
crisitem.author.orcid0000-0002-9321-3464-
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