Model selection for semi-supervised clustering (2014)
- Authors:
- Autor USP: CAMPELLO, RICARDO JOSÉ GABRIELLI BARRETO - ICMC
- Unidade: ICMC
- DOI: 10.5441/002/edbt.2014.31
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Publisher: OpenProceedings
- Publisher place: Konstanz
- Date published: 2014
- ISBN: 9783893180653
- Source:
- Título: Proceedings
- Conference titles: International Conference on Extending Database Technology - EDBT
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
POURRAJABI, Mojgan et al. Model selection for semi-supervised clustering. 2014, Anais.. Konstanz: OpenProceedings, 2014. Disponível em: https://doi.org/10.5441/002/edbt.2014.31. Acesso em: 10 jan. 2026. -
APA
Pourrajabi, M., Moulavi, D., Campello, R. J. G. B., Zimek, A., Sander, J., & Goebel, R. (2014). Model selection for semi-supervised clustering. In Proceedings. Konstanz: OpenProceedings. doi:10.5441/002/edbt.2014.31 -
NLM
Pourrajabi M, Moulavi D, Campello RJGB, Zimek A, Sander J, Goebel R. Model selection for semi-supervised clustering [Internet]. Proceedings. 2014 ;[citado 2026 jan. 10 ] Available from: https://doi.org/10.5441/002/edbt.2014.31 -
Vancouver
Pourrajabi M, Moulavi D, Campello RJGB, Zimek A, Sander J, Goebel R. Model selection for semi-supervised clustering [Internet]. Proceedings. 2014 ;[citado 2026 jan. 10 ] Available from: https://doi.org/10.5441/002/edbt.2014.31 - A cluster based hybrid feature selection approach
- Ensembles for unsupervised outlier detection: challenges and research questions
- Communities validity: methodical evaluation of community mining algorithms
- Efficient computation of multiple density-based clustering hierarchies
- Relative validity criteria for community mining algorithms
- Identificação e controle de processos via desenvolvimentos em séries ortonormais: parte B: controle preditivo
- A fuzzy extension of the rand index and other related indexes for clustering and classification assessment
- On the selection of appropriate distances for gene expression data clustering
- Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis
- Clustering of RNA-Seq samples: comparison study on cancer data
Informações sobre o DOI: 10.5441/002/edbt.2014.31 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
