Relative validity criteria for community mining algorithms (2014)
- Authors:
- Autor USP: CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- DOI: 10.1007/978-1-4614-6170-8_356
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- ISBN: 9781461461692
- Source:
- Título: Encyclopedia of social network analysis and mining
- Volume/Número/Paginação/Ano: 2437 p
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
RABBANY, Reihaneh et al. Relative validity criteria for community mining algorithms. Encyclopedia of social network analysis and mining. Tradução . New York: Springer, 2014. . Disponível em: https://doi.org/10.1007/978-1-4614-6170-8_356. Acesso em: 09 fev. 2026. -
APA
Rabbany, R., Takaffoli, M., Fagnan, J., Zaïane, O. R., & Campello, R. J. G. B. (2014). Relative validity criteria for community mining algorithms. In Encyclopedia of social network analysis and mining. New York: Springer. doi:10.1007/978-1-4614-6170-8_356 -
NLM
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB. Relative validity criteria for community mining algorithms [Internet]. In: Encyclopedia of social network analysis and mining. New York: Springer; 2014. [citado 2026 fev. 09 ] Available from: https://doi.org/10.1007/978-1-4614-6170-8_356 -
Vancouver
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB. Relative validity criteria for community mining algorithms [Internet]. In: Encyclopedia of social network analysis and mining. New York: Springer; 2014. [citado 2026 fev. 09 ] Available from: https://doi.org/10.1007/978-1-4614-6170-8_356 - Similarity measures for comparing biclusterings
- Density-based clustering validation
- Active learning strategies for semi-supervised DBSCAN
- On the evaluation of outlier detection and one-class classification methods
- An introduction to models based on Laguerre, Kautz and other related orthonormal functions - part II: non-linear models
- Evaluating correlation coefficients for clustering gene expression profiles of cancer
- A simpler and more accurate AUTO-HDS framework for clustering and visualization of biological data
- Exact search directions for optimization of linear and nonlinear models based on generalized orthonormal functions
- A cluster based hybrid feature selection approach
- A unified framework of density-based clustering for semi-supervised classification
Informações sobre o DOI: 10.1007/978-1-4614-6170-8_356 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
