A Hybrid Heuristic for the k-medoids Clustering Problem

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dc.contributor.authorNascimento, Maria C. V. [UNIFESP]
dc.contributor.authorToledo, Franklina M. B.
dc.contributor.authorCarvalho, Andre C. P. L. F. de
dc.contributor.authorSoule, T.
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.date.accessioned2016-01-24T14:17:36Z
dc.date.available2016-01-24T14:17:36Z
dc.date.issued2012-01-01
dc.description.abstractClustering is an important tool for data analysis, since it allows the exploration of datasets with no or very little prior information. Its main goal is to group a set of data based on their similarity (dissimilarity). A well known mathematical formulation for clustering is the k-medoids problem. Current versions of k-medoids rely on heuristics, with good results reported in the literature. However, few methods that analyze the quality of the partitions found by the heuristics have been proposed. in this paper, we propose a hybrid Lagrangian heuristic for the k-medoids. We compare the performance of the proposed Lagrangian heuristic with other heuristics for the k-medoids problem found in literature. Experimental results presented that the proposed Lagrangian heuristic outperformed the other algorithms.en
dc.description.affiliationUNIFESP, Inst Ciencia & Tecnol, BR-12230280 Sao Jose Dos Campos, SP, Brazil
dc.description.affiliationUnifespUNIFESP, Inst Ciencia & Tecnol, ICT, BR-12230280 Sao Jose Dos Campos, SP, Brazil
dc.description.sourceWeb of Science
dc.format.extent417-424
dc.identifierhttp://dx.doi.org/10.1145/2330163.2330223
dc.identifier.citationProceedings of the Fourteenth International Conference On Genetic and Evolutionary Computation Conference. New York: Assoc Computing Machinery, p. 417-424, 2012.
dc.identifier.doi10.1145/2330163.2330223
dc.identifier.fileWOS000309611100053.pdf
dc.identifier.urihttp://repositorio.unifesp.br/handle/11600/34362
dc.identifier.wosWOS:000309611100053
dc.language.isoeng
dc.publisherAssoc Computing Machinery
dc.relation.ispartofProceedings of the Fourteenth International Conference On Genetic and Evolutionary Computation Conference
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectclusteringen
dc.subjectbioinformaticsen
dc.subjectheuristicen
dc.subjectPAMen
dc.subjectinteger programmingen
dc.titleA Hybrid Heuristic for the k-medoids Clustering Problemen
dc.typeinfo:eu-repo/semantics/conferenceObject
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