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
- Unidade: ICMC
- DOI: 10.1109/ICDMW.2016.0093
- Subjects: INTELIGÊNCIA ARTIFICIAL; FRACTAIS
- Language: Inglês
- Imprenta:
- Publisher: IEEE
- Publisher place: Los Alamitos
- Date published: 2016
- Source:
- Título: Proceedings
- ISSN: 2375-9259
- Conference titles: International Conference on Data Mining Workshops - ICDMW
- Este periódico é de acesso aberto
- Este artigo NÃO é de acesso aberto
-
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: https://doi.org/10.1109/ICDMW.2016.0093. Acesso em: 11 fev. 2026. -
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 2026 fev. 11 ] Available from: https://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 2026 fev. 11 ] Available from: https://doi.org/10.1109/ICDMW.2016.0093 - ORFEL: efficient detection of defamation or illegitimate promotion in online recommendation
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Informações sobre o DOI: 10.1109/ICDMW.2016.0093 (Fonte: oaDOI API)
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