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dc.contributor.authorAlguwaizani, Abdulrahmanen
dc.contributor.authorHansen, Pierreen
dc.contributor.authorMladenović, Nenaden
dc.contributor.authorNgai, Ericen
dc.date.accessioned2020-05-02T16:42:07Z-
dc.date.available2020-05-02T16:42:07Z-
dc.date.issued2011-01-01en
dc.identifier.issn0307-904Xen
dc.identifier.urihttp://researchrepository.mi.sanu.ac.rs/handle/123456789/2499-
dc.description.abstractHarmonic means clustering is a variant of minimum sum of squares clustering (which is sometimes called K-means clustering), designed to alleviate the dependance of the results on the choice of the initial solution. In the harmonic means clustering problem, the sum of harmonic averages of the distances from the data points to all cluster centroids is minimized. In this paper, we propose a variable neighborhood search heuristic for solving it. This heuristic has been tested on numerous datasets from the literature. It appears that our results compare favorably with recent ones from tabu search and simulated annealing heuristics.en
dc.publisherElsevier-
dc.relation.ispartofApplied Mathematical Modellingen
dc.subjectClustering | K-harmonic means | Metaheuristics | Minimum sum of squares | Unsupervised learning | Variable neighborhood searchen
dc.titleVariable neighborhood search for harmonic means clusteringen
dc.typeArticleen
dc.identifier.doi10.1016/j.apm.2010.11.032en
dc.identifier.scopus2-s2.0-79951942171en
dc.contributor.affiliationMathematical Institute of the Serbian Academy of Sciences and Arts-
dc.relation.firstpage2688en
dc.relation.lastpage2694en
dc.relation.issue6en
dc.relation.volume35en
dc.description.rankM21-
item.cerifentitytypePublications-
item.openairetypeArticle-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
crisitem.author.orcid0000-0001-6655-0409-
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