Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations (2016)
- Authors:
- USP affiliated authors: RODRIGUES JUNIOR, JOSÉ FERNANDO - ICMC ; CORDEIRO, ROBSON LEONARDO FERREIRA - ICMC
- School: ICMC
- DOI: 10.1109/ICDMW.2016.0093
- Subjects: INTELIGÊNCIA ARTIFICIAL; FRACTAIS
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Place of publication: Los Alamitos
- Date published: 2016
- Source:
- Título do periódico: Proceedings
- ISSN: 2375-9259
- Conference title: International Conference on Data Mining Workshops - ICDMW
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
-
ABNT
FRAIDEINBERZE, Antonio C e RODRIGUES JUNIOR, José Fernando e CORDEIRO, Robson Leonardo Ferreira. Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations. 2016, Anais.. Los Alamitos: IEEE, 2016. Disponível em: http://dx.doi.org/10.1109/ICDMW.2016.0093. Acesso em: 04 jul. 2022. -
APA
Fraideinberze, A. C., Rodrigues Junior, J. F., & Cordeiro, R. L. F. (2016). Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations. In Proceedings. Los Alamitos: IEEE. doi:10.1109/ICDMW.2016.0093 -
NLM
Fraideinberze AC, Rodrigues Junior JF, Cordeiro RLF. Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations [Internet]. Proceedings. 2016 ;[citado 2022 jul. 04 ] Available from: http://dx.doi.org/10.1109/ICDMW.2016.0093 -
Vancouver
Fraideinberze AC, Rodrigues Junior JF, Cordeiro RLF. Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations [Internet]. Proceedings. 2016 ;[citado 2022 jul. 04 ] Available from: http://dx.doi.org/10.1109/ICDMW.2016.0093 - StructMatrix: large-scale visualization of graphs by means of structure detection and dense matrices
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Informações sobre o DOI: 10.1109/ICDMW.2016.0093 (Fonte: oaDOI API)
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