An evolutionary algorithm for clustering data streams with a variable number of clusters (2017)
- Authors:
- Autor USP: HRUSCHKA, EDUARDO RAUL - ICMC
- Unidade: ICMC
- DOI: 10.1016/j.eswa.2016.09.020
- Subjects: INTELIGÊNCIA ARTIFICIAL; ALGORITMOS GENÉTICOS
- Keywords: Evolutionary algorithms; Clustering; Data streams; Concept drift
- Language: Inglês
- Imprenta:
- Source:
- Título: Expert Systems with Applications
- ISSN: 0957-4174
- Volume/Número/Paginação/Ano: v. 67, p. 228-238, Jan. 2017
- Este periódico é de assinatura
- Este artigo NÃO é de acesso aberto
- Cor do Acesso Aberto: closed
-
ABNT
SILVA, Jonathan de Andrade e HRUSCHKA, Eduardo Raul e GAMA, João. An evolutionary algorithm for clustering data streams with a variable number of clusters. Expert Systems with Applications, v. 67, n. Ja 2017, p. 228-238, 2017Tradução . . Disponível em: https://doi.org/10.1016/j.eswa.2016.09.020. Acesso em: 10 out. 2024. -
APA
Silva, J. de A., Hruschka, E. R., & Gama, J. (2017). An evolutionary algorithm for clustering data streams with a variable number of clusters. Expert Systems with Applications, 67( Ja 2017), 228-238. doi:10.1016/j.eswa.2016.09.020 -
NLM
Silva J de A, Hruschka ER, Gama J. An evolutionary algorithm for clustering data streams with a variable number of clusters [Internet]. Expert Systems with Applications. 2017 ; 67( Ja 2017): 228-238.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.eswa.2016.09.020 -
Vancouver
Silva J de A, Hruschka ER, Gama J. An evolutionary algorithm for clustering data streams with a variable number of clusters [Internet]. Expert Systems with Applications. 2017 ; 67( Ja 2017): 228-238.[citado 2024 out. 10 ] Available from: https://doi.org/10.1016/j.eswa.2016.09.020 - An experimental study on the use of nearest neighbor-based imputation algorithms for classification tasks
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Informações sobre o DOI: 10.1016/j.eswa.2016.09.020 (Fonte: oaDOI API)
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