A study of K-means-based algorithms for constrained clustering (2013)
- Authors:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.3233/IDA-130590
- Assunto: INTELIGÊNCIA ARTIFICIAL
- Language: Inglês
- Imprenta:
- Source:
- Título: Intelligent Data Analysis
- ISSN: 1088-467X
- Volume/Número/Paginação/Ano: v. 17, n. 3, p. 485-505, 2013
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
ABNT
COVÕES, Thiago Ferreira e HRUSCHKA, Eduardo Raul e GHOSH, Joydeep. A study of K-means-based algorithms for constrained clustering. Intelligent Data Analysis, v. 17, n. 3, p. 485-505, 2013Tradução . . Disponível em: https://doi.org/10.3233/IDA-130590. Acesso em: 13 fev. 2026. -
APA
Covões, T. F., Hruschka, E. R., & Ghosh, J. (2013). A study of K-means-based algorithms for constrained clustering. Intelligent Data Analysis, 17( 3), 485-505. doi:10.3233/IDA-130590 -
NLM
Covões TF, Hruschka ER, Ghosh J. A study of K-means-based algorithms for constrained clustering [Internet]. Intelligent Data Analysis. 2013 ; 17( 3): 485-505.[citado 2026 fev. 13 ] Available from: https://doi.org/10.3233/IDA-130590 -
Vancouver
Covões TF, Hruschka ER, Ghosh J. A study of K-means-based algorithms for constrained clustering [Internet]. Intelligent Data Analysis. 2013 ; 17( 3): 485-505.[citado 2026 fev. 13 ] Available from: https://doi.org/10.3233/IDA-130590 - On the influence of imputation in classification: practical issues
- An evolutionary algorithm for clustering data streams with a variable number of clusters
- An Experimental Study on Unsupervised Clustering-Based Feature Selection Methods
- Using both latent and supervised shared topics for multitask learning
- Biocom_Usp: tweet sentiment analysis with adaptive boosting ensemble
- A semi-supervised approach to estimate the number of clusters per class
- Classification with multi-modal classes using evolutionary algorithms and constrained clustering
- A distance-based mutation operator for learning bayesian network structures using evolutionary algorithms
- Evolving Gaussian mixture models with splitting and merging mutation operators
- A cluster-based feature selection approach
Informações sobre o DOI: 10.3233/IDA-130590 (Fonte: oaDOI API)
How to cite
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
