A robust methodology for comparing performances of clustering validity criteria (2008)
- Authors:
- USP affiliated authors: CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC ; HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-3-540-88190-2_29
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: Springer-Verlag
- Publisher place: Heidelberg
- Date published: 2008
- Source:
- Título do periódico: Lecture Notes in Artificial Intelligence - LNAI
- Volume/Número/Paginação/Ano: v. 5249, p. 237-247, 2008
- Conference titles: Brazilian Symposium on Artificial Intelligence - SBIA 2008
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
VENDRAMIN, Lucas e CAMPELLO, Ricardo José Gabrielli Barreto e HRUSCHKA, Eduardo Raul. A robust methodology for comparing performances of clustering validity criteria. Lecture Notes in Artificial Intelligence - LNAI. Heidelberg: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-540-88190-2_29. Acesso em: 19 set. 2024. , 2008 -
APA
Vendramin, L., Campello, R. J. G. B., & Hruschka, E. R. (2008). A robust methodology for comparing performances of clustering validity criteria. Lecture Notes in Artificial Intelligence - LNAI. Heidelberg: Springer-Verlag. doi:10.1007/978-3-540-88190-2_29 -
NLM
Vendramin L, Campello RJGB, Hruschka ER. A robust methodology for comparing performances of clustering validity criteria [Internet]. Lecture Notes in Artificial Intelligence - LNAI. 2008 ; 5249 237-247.[citado 2024 set. 19 ] Available from: https://doi.org/10.1007/978-3-540-88190-2_29 -
Vancouver
Vendramin L, Campello RJGB, Hruschka ER. A robust methodology for comparing performances of clustering validity criteria [Internet]. Lecture Notes in Artificial Intelligence - LNAI. 2008 ; 5249 237-247.[citado 2024 set. 19 ] Available from: https://doi.org/10.1007/978-3-540-88190-2_29 - Relative clustering validity criteria: a comparative overview
- 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
- Distributed Fuzzy clustering with automatic detection of the number of clusters
- On the efficiency of evolutionary fuzzy clustering
- On the comparisson of relative clustering validity criteria
- A fuzzy variant of an evolutionary algorithm for clustering
- Fuzzy clustering-based filter
- Collaborative fuzzy clustering algorithms: some refinements and design guidelines
- Efficiency issues of evolutionary k-means
Informações sobre o DOI: 10.1007/978-3-540-88190-2_29 (Fonte: oaDOI API)
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