A bee-inspired multiobjective optimization clustering algorithm

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dc.creatorDávila Patrícia Ferreira Cruz
dc.creatorAlexandre Alberto Politi
dc.creatorDanilo Cunha
dc.creator‪Leandro Nunes de Castro
dc.creatorRenato Dourado Maia
dc.date.accessioned2022-07-04T13:26:24Z
dc.date.accessioned2025-09-09T00:30:54Z
dc.date.available2022-07-04T13:26:24Z
dc.date.issued2016-08
dc.identifier.doi10.2316/P.2016.841-035
dc.identifier.isbn9780889869837
dc.identifier.urihttps://hdl.handle.net/1843/42873
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofModelling, Simulation and Identification - MSI 2016
dc.rightsAcesso Restrito
dc.subjectAlgoritmos
dc.subjectCluster (Sistema de computador)
dc.titleA bee-inspired multiobjective optimization clustering algorithm
dc.typeArtigo de evento
local.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.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.actapress.com/Abstract.aspx?paperId=456259

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