Distributed Fuzzy clustering with automatic detection of the number of clusters (2011)
- Authors:
- USP affiliated authors: CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC ; HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-642-19934-9_17
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: Springer-Verlag
- Publisher place: Heidelberg
- Date published: 2011
- Source:
- Título do periódico: Advances in Intelligent and Soft Computing
- ISSN: 1867-5662
- Volume/Número/Paginação/Ano: v. 91, p. 133-140 , 2011
- Conference titles: International Symposium on Distributed Computing and Artificial Intelligence - DCAI 2011
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
VENDRAMIN, Lucas; CAMPELLO, Ricardo José Gabrielli Barreto; COLETTA, Luiz Fernando Sommaggio; HRUSCHKA, Eduardo Raul. Distributed Fuzzy clustering with automatic detection of the number of clusters. Advances in Intelligent and Soft Computing[S.l: s.n.], 2011.Disponível em: -
APA
Vendramin, L., Campello, R. J. G. B., Coletta, L. F. S., & Hruschka, E. R. (2011). Distributed Fuzzy clustering with automatic detection of the number of clusters. Advances in Intelligent and Soft Computing. Heidelberg: Springer-Verlag. doi:10.1007/978-3-642-19934-9_17 -
NLM
Vendramin L, Campello RJGB, Coletta LFS, Hruschka ER. Distributed Fuzzy clustering with automatic detection of the number of clusters [Internet]. Advances in Intelligent and Soft Computing. 2011 ; 91 133-140 .Available from: http://dx.doi.org/10.1007/978-3-642-19934-9_17 -
Vancouver
Vendramin L, Campello RJGB, Coletta LFS, Hruschka ER. Distributed Fuzzy clustering with automatic detection of the number of clusters [Internet]. Advances in Intelligent and Soft Computing. 2011 ; 91 133-140 .Available from: http://dx.doi.org/10.1007/978-3-642-19934-9_17 - Relative clustering validity criteria: a comparative overview
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- On the comparisson of relative clustering validity criteria
- Fuzzy clustering-based filter
- Collaborative fuzzy clustering algorithms: some refinements and design guidelines
- On comparing two sequences of numbers and its applications to clustering analysis
- A comparative study on the use of correlation coefficients for redundant feature elimination
- Efficiency issues of evolutionary k-means
- Evolutionary fuzzy clustering: an overview and efficiency issues
- A survey of evolutionary algorithms for clustering
Informações sobre o DOI: 10.1007/978-3-642-19934-9_17 (Fonte: oaDOI API)
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