Source: Proceedings. Conference titles: International Conference on Data Mining Workshops - ICDMW. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, FRACTAIS
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: 28 nov. 2025.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.2016.0093NLM
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 2025 nov. 28 ] Available from: https://doi.org/10.1109/ICDMW.2016.0093Vancouver
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 2025 nov. 28 ] Available from: https://doi.org/10.1109/ICDMW.2016.0093
