Please use this identifier to cite or link to this item: http://hdl.handle.net/11422/1894
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dc.contributor.authorFranco, Cláudia Rita de-
dc.contributor.authorVidal, Leonardo Silva-
dc.contributor.authorCruz, Adriano Joaquim de Oliveira-
dc.date.accessioned2017-05-08T15:07:01Z-
dc.date.available2023-12-21T03:00:54Z-
dc.date.issued2002-12-30-
dc.identifier.citationFRANCO, C. R. de.; VIDAL, L. S.; CRUZ, A. J. DE O. A validity measure for hard and fuzzy clustering derived from Fischer's linear discriminant. Rio de Janeiro: NCE/UFRJ, 2002. 6 p. (Relatório Técnico, 02/02)pt_BR
dc.identifier.urihttp://hdl.handle.net/11422/1894-
dc.description.abstractCluster analysis has a growing importance in many research areas, especially those involving problems of pattern recognition. Generally, in real world problems, the number of classes is unknown in advance, being necessary to have criterions to Identify the best choice of clusters. Here we propose an extension to Fisher Linear Discriminant, the EFLD that does not impose limits on the minimum number of samples, can be applied to fuzzy and crisp partitions and can be calculated more efficiently. We also propose a nem fast and efficient validity method based in the EFLD that measures the compactness and separation of partitions produced by any fuzzy or crisp clustering algorithm. The simulations performed indicate that it's a efficient and fast measure even when the overlapping between clusters is high. Finally, we propose an algorithm that applies the new validity measure to the problem of finding the patterns for the fuzzy K-NN classifier. This algorithm is applied to the problem of cursive digits recognition.pt_BR
dc.languageengpt_BR
dc.relation.ispartofRelatório Técnico NCEpt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectCluster validityen
dc.subjectFuzzy clusteringen
dc.subjectPattern recognitionen
dc.subjectCursive digits recognitionen
dc.subjectFisher's linear discriminanten
dc.subjectAgrupamento difusopt_BR
dc.subjectSistemas de reconhecimento de padrõespt_BR
dc.titleA validity measure for hard and fuzzy clustering derived from Fisher's linear discriminantpt_BR
dc.typeRelatóriopt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentInstituto Tércio Pacitti de Aplicações e Pesquisas Computacionaispt_BR
dc.subject.cnpqCNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOpt_BR
dc.citation.issue0202pt_BR
dc.embargo.termsabertopt_BR
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