Bio-inspired multiobjective clustering optimization: a survey and a proposal

Página do item simplificado
dc.creatorDanilo Cunha
dc.creatorDávila Cruz
dc.creatorAlexandre Politi
dc.creatorLeandro Nunes de Castro
dc.creatorRenato Dourado Maia
dc.date.accessioned2022-07-05T14:13:32Z
dc.date.accessioned2025-09-08T23:52:54Z
dc.date.available2022-07-05T14:13:32Z
dc.date.issued2017
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.description.sponsorshipFAPESP - Fundação de Amparo à Pesquisa do Estado de São Paulo
dc.description.sponsorshipOutra Agência
dc.identifier.doihttps://doi.org/10.5430/air.v6n2p10
dc.identifier.issn1927-6982
dc.identifier.urihttps://hdl.handle.net/1843/42910
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofArtificial Intelligence Research
dc.rightsAcesso Aberto
dc.subjectAnálise por agrupamento
dc.subjectCluster (Sistema de computador)
dc.titleBio-inspired multiobjective clustering optimization: a survey and a proposal
dc.typeArtigo de periódico
local.citation.epage26
local.citation.issue2
local.citation.spage10
local.citation.volume6
local.description.resumoMultiobjective clustering techniques have been used to simultaneously consider several complementary aspects of clustering quality. They optimize two or more cluster validity indices simultaneously, they lead to high-quality results, and have emerged as attractive and robust alternatives for solving clustering problems. This paper provides a brief review of bio-Inspired multiobjective clustering, and proposes a bee-inspired multiobjective optimization (MOO) algorithm, named cOptBees-MO, to solve multiobjective data clustering problems. In its survey part, a brief tutorial on MOO and multiobjective clustering optimization (MOCO) is presented, followed by a review of the main works in the area. Particular attention is given to the many objective functions used in MOCO. To evaluate the performance of the algorithm it was executed for various datasets and the results presented high quality clusters, diverse solutions an the automatic determination of a suitable number of clusters.
local.publisher.countryBrasil
local.publisher.departmentICA - INSTITUTO DE CIÊNCIAS AGRÁRIAS
local.publisher.initialsUFMG
local.url.externahttps://www.sciedu.ca/journal/index.php/air/article/view/10658

Arquivos

Pacote original

Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
Bio-inspired multiobjective clustering optimization a survey and a proposal.pdf
Tamanho:
1.03 MB
Formato:
Adobe Portable Document Format
Baixar

Licença do pacote

Agora exibindo 1 - 1 de 1
Imagem de Miniatura
Nome:
License.txt
Tamanho:
1.99 KB
Formato:
Plain Text
Descrição:
Baixar

Coleções

Artigo de Periódico

Logo UFMG