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Dissertação (Mestrado)
Unsupervised Dimensionality Reduction in Big Data via Massive Parallel Processing with MapReduce and Resilient Distributed Datasets (2020)
Oliveira, Jadson Jose Monteiro; Cordeiro, Robson Leonardo Ferreira (Orientador)
Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, FRACTAIS, BIG DATA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA, Jadson Jose Monteiro. Unsupervised Dimensionality Reduction in Big Data via Massive Parallel Processing with MapReduce and Resilient Distributed Datasets. 2020. Dissertação (Mestrado) – Universidade de São Paulo, São Carlos, 2020. Disponível em: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20012021-125711/. Acesso em: 25 abr. 2026.
APA
Oliveira, J. J. M. (2020). Unsupervised Dimensionality Reduction in Big Data via Massive Parallel Processing with MapReduce and Resilient Distributed Datasets (Dissertação (Mestrado). Universidade de São Paulo, São Carlos. Recuperado de https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20012021-125711/
NLM
Oliveira JJM. Unsupervised Dimensionality Reduction in Big Data via Massive Parallel Processing with MapReduce and Resilient Distributed Datasets [Internet]. 2020 ;[citado 2026 abr. 25 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20012021-125711/
Vancouver
Oliveira JJM. Unsupervised Dimensionality Reduction in Big Data via Massive Parallel Processing with MapReduce and Resilient Distributed Datasets [Internet]. 2020 ;[citado 2026 abr. 25 ] Available from: https://www.teses.usp.br/teses/disponiveis/55/55134/tde-20012021-125711/
Artigo de periodico
Unsupervised dimensionality reduction for very large datasets: are we going to the right direction? (2020)
Oliveira, Jadson José Monteiro ; Cordeiro, Robson Leonardo Ferreira
Source: Knowledge-Based Systems. Unidade: ICMC
Subjects: BANCO DE DADOS, MINERAÇÃO DE DADOS, FRACTAIS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
OLIVEIRA, Jadson José Monteiro e CORDEIRO, Robson Leonardo Ferreira. Unsupervised dimensionality reduction for very large datasets: are we going to the right direction?. Knowledge-Based Systems, v. 196, p. 1-14, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.knosys.2020.105777. Acesso em: 25 abr. 2026.
APA
Oliveira, J. J. M., & Cordeiro, R. L. F. (2020). Unsupervised dimensionality reduction for very large datasets: are we going to the right direction? Knowledge-Based Systems, 196, 1-14. doi:10.1016/j.knosys.2020.105777
NLM
Oliveira JJM, Cordeiro RLF. Unsupervised dimensionality reduction for very large datasets: are we going to the right direction? [Internet]. Knowledge-Based Systems. 2020 ; 196 1-14.[citado 2026 abr. 25 ] Available from: https://doi.org/10.1016/j.knosys.2020.105777
Vancouver
Oliveira JJM, Cordeiro RLF. Unsupervised dimensionality reduction for very large datasets: are we going to the right direction? [Internet]. Knowledge-Based Systems. 2020 ; 196 1-14.[citado 2026 abr. 25 ] Available from: https://doi.org/10.1016/j.knosys.2020.105777
Dissertação (Mestrado)
Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations (2017)
Fraideinberze, Antonio Canabrava; Cordeiro, Robson Leonardo Ferreira (Orientador)
Unidade: ICMC
Subjects: FRACTAIS, BANCO DE DADOS, ALGORITMOS E ESTRUTURAS DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FRAIDEINBERZE, Antonio Canabrava. Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations. 2017. Dissertação (Mestrado) – Universidade de São Paulo, São Carlos, 2017. Disponível em: https://teses.usp.br/teses/disponiveis/55/55134/tde-17112017-154451/. Acesso em: 25 abr. 2026.
APA
Fraideinberze, A. C. (2017). Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations (Dissertação (Mestrado). Universidade de São Paulo, São Carlos. Recuperado de https://teses.usp.br/teses/disponiveis/55/55134/tde-17112017-154451/
NLM
Fraideinberze AC. Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations [Internet]. 2017 ;[citado 2026 abr. 25 ] Available from: https://teses.usp.br/teses/disponiveis/55/55134/tde-17112017-154451/
Vancouver
Fraideinberze AC. Effective and unsupervised fractal-based feature selection for very large datasets: removing linear and non-linear attribute correlations [Internet]. 2017 ;[citado 2026 abr. 25 ] Available from: https://teses.usp.br/teses/disponiveis/55/55134/tde-17112017-154451/

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