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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/42873
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dc.creatorDávila Patrícia Ferreira Cruzpt_BR
dc.creatorAlexandre Alberto Politipt_BR
dc.creatorDanilo Cunhapt_BR
dc.creator‪Leandro Nunes de Castropt_BR
dc.creatorRenato Dourado Maiapt_BR
dc.date.accessioned2022-07-04T13:26:24Z-
dc.date.available2022-07-04T13:26:24Z-
dc.date.issued2016-08-
dc.identifier.doi10.2316/P.2016.841-035pt_BR
dc.identifier.isbn9780889869837pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/42873-
dc.description.resumoMultiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize more than one cluster validity index simultaneously, leading to high-quality re-sults, and have emerged as attractive and robust alternatives for clustering problems. This paper proposes a bee-inspired multiobjective optimization algorithm to solve data clustering problems. The algorithm was run for different datasets and the results obtained showed high quality clusters and diversity of solutions, whilst a suitable number of clusters was automatically determined.pt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIASpt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofModelling, Simulation and Identification - MSI 2016pt_BR
dc.rightsAcesso Restritopt_BR
dc.subject.otherAlgoritmospt_BR
dc.subject.otherCluster (Sistema de computador)pt_BR
dc.titleA bee-inspired multiobjective optimization clustering algorithmpt_BR
dc.typeArtigo de Eventopt_BR
dc.url.externahttps://www.actapress.com/Abstract.aspx?paperId=456259pt_BR
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