Relative validity criteria for community mining algorithms (2012)
- Authors:
- Autor USP: CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- DOI: 10.1109/ASONAM.2012.52
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Piscataway
- Date published: 2012
- ISBN: 9780769547992
- Source:
- Título: Proceedings
- Conference titles: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining - ASONAM
- Este artigo NÃO possui versão em acesso aberto
-
Status: Nenhuma versão em acesso aberto identificada -
ABNT
RABBANY, Reihaneh et al. Relative validity criteria for community mining algorithms. 2012, Anais.. Piscataway: IEEE, 2012. Disponível em: https://doi.org/10.1109/ASONAM.2012.52. Acesso em: 15 mar. 2026. -
APA
Rabbany, R., Takaffoli, M., Fagnan, J., Zaïane, O. R., & Campello, R. J. G. B. (2012). Relative validity criteria for community mining algorithms. In Proceedings. Piscataway: IEEE. doi:10.1109/ASONAM.2012.52 -
NLM
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB. Relative validity criteria for community mining algorithms [Internet]. Proceedings. 2012 ;[citado 2026 mar. 15 ] Available from: https://doi.org/10.1109/ASONAM.2012.52 -
Vancouver
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB. Relative validity criteria for community mining algorithms [Internet]. Proceedings. 2012 ;[citado 2026 mar. 15 ] Available from: https://doi.org/10.1109/ASONAM.2012.52 - 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 a disponibilidade de versões do artigo em acesso aberto coletadas automaticamente via oaDOI API (Unpaywall).
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
