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Filtros : "Data Mining and Knowledge Discovery" "Financiamento FAPESP" Limpar

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  • Artigo de periodico

    ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers (2025)

    Cavalcanti, Douglas Monteiro; Cerri, Ricardo ; Faria, Elaine Ribeiro

    Source: Data Mining and Knowledge Discovery. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, FRAMEWORKS

    PrivadoAcesso à fonteDOIHow to cite
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    • ABNT

      CAVALCANTI, Douglas Monteiro e CERRI, Ricardo e FARIA, Elaine Ribeiro. ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers. Data Mining and Knowledge Discovery, v. 39, n. 5, p. 1-35, 2025Tradução . . Disponível em: https://doi.org/10.1007/s10618-025-01124-4. Acesso em: 11 nov. 2025.

    • APA

      Cavalcanti, D. M., Cerri, R., & Faria, E. R. (2025). ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers. Data Mining and Knowledge Discovery, 39( 5), 1-35. doi:10.1007/s10618-025-01124-4

    • NLM

      Cavalcanti DM, Cerri R, Faria ER. ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers [Internet]. Data Mining and Knowledge Discovery. 2025 ; 39( 5): 1-35.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-025-01124-4

    • Vancouver

      Cavalcanti DM, Cerri R, Faria ER. ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers [Internet]. Data Mining and Knowledge Discovery. 2025 ; 39( 5): 1-35.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-025-01124-4

  • Artigo de periodico

    Efficient outlier detection in numerical and categorical data (2025)

    Cabral, Eugenio Ferreira; Sánchez Vinces, Braulio Valentin ; Silva, Guilherme Domingos Faria; Sander, Jörg ; Cordeiro, Robson Leonardo Ferreira

    Source: Data Mining and Knowledge Discovery. Unidade: ICMC

    Subjects: BANCO DE DADOS, MINERAÇÃO DE DADOS

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    • ABNT

      CABRAL, Eugenio Ferreira et al. Efficient outlier detection in numerical and categorical data. Data Mining and Knowledge Discovery, v. 39, p. 1-46, 2025Tradução . . Disponível em: https://doi.org/10.1007/s10618-024-01084-1. Acesso em: 11 nov. 2025.

    • APA

      Cabral, E. F., Sánchez Vinces, B. V., Silva, G. D. F., Sander, J., & Cordeiro, R. L. F. (2025). Efficient outlier detection in numerical and categorical data. Data Mining and Knowledge Discovery, 39, 1-46. doi:10.1007/s10618-024-01084-1

    • NLM

      Cabral EF, Sánchez Vinces BV, Silva GDF, Sander J, Cordeiro RLF. Efficient outlier detection in numerical and categorical data [Internet]. Data Mining and Knowledge Discovery. 2025 ; 39 1-46.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-024-01084-1

    • Vancouver

      Cabral EF, Sánchez Vinces BV, Silva GDF, Sander J, Cordeiro RLF. Efficient outlier detection in numerical and categorical data [Internet]. Data Mining and Knowledge Discovery. 2025 ; 39 1-46.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-024-01084-1

  • Artigo de periodico

    Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms (2024)

    Mantovani, Rafael Gomes ; Horváth, Tomás ; Rossi, André Luis Debiaso ; Cerri, Ricardo ; Barbon Júnior, Sylvio ; Vanschoren, Joaquin ; Carvalho, André Carlos Ponce de Leon Ferreira de

    Source: Data Mining and Knowledge Discovery. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS

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    • ABNT

      MANTOVANI, Rafael Gomes et al. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, v. 38, n. 3, p. 1364-1416, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-024-01002-5. Acesso em: 11 nov. 2025.

    • APA

      Mantovani, R. G., Horváth, T., Rossi, A. L. D., Cerri, R., Barbon Júnior, S., Vanschoren, J., & Carvalho, A. C. P. de L. F. de. (2024). Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery, 38( 3), 1364-1416. doi:10.1007/s10618-024-01002-5

    • NLM

      Mantovani RG, Horváth T, Rossi ALD, Cerri R, Barbon Júnior S, Vanschoren J, Carvalho ACP de LF de. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38( 3): 1364-1416.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-024-01002-5

    • Vancouver

      Mantovani RG, Horváth T, Rossi ALD, Cerri R, Barbon Júnior S, Vanschoren J, Carvalho ACP de LF de. Better trees: an empirical study on hyperparameter tuning of classification decision tree induction algorithms [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38( 3): 1364-1416.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-024-01002-5

  • Artigo de periodico

    Unsupervised feature based algorithms for time series extrinsic regression (2024)

    Guijo-Rubio, David ; Middlehurst, Matthew ; Arcencio, Guilherme ; Silva, Diego Furtado ; Bagnall, Anthony

    Source: Data Mining and Knowledge Discovery. Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, ALGORITMOS ÚTEIS E ESPECÍFICOS

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    • ABNT

      GUIJO-RUBIO, David et al. Unsupervised feature based algorithms for time series extrinsic regression. Data Mining and Knowledge Discovery, v. 38, n. 4, p. 2141-2185, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-024-01027-w. Acesso em: 11 nov. 2025.

    • APA

      Guijo-Rubio, D., Middlehurst, M., Arcencio, G., Silva, D. F., & Bagnall, A. (2024). Unsupervised feature based algorithms for time series extrinsic regression. Data Mining and Knowledge Discovery, 38( 4), 2141-2185. doi:10.1007/s10618-024-01027-w

    • NLM

      Guijo-Rubio D, Middlehurst M, Arcencio G, Silva DF, Bagnall A. Unsupervised feature based algorithms for time series extrinsic regression [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38( 4): 2141-2185.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-024-01027-w

    • Vancouver

      Guijo-Rubio D, Middlehurst M, Arcencio G, Silva DF, Bagnall A. Unsupervised feature based algorithms for time series extrinsic regression [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38( 4): 2141-2185.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-024-01027-w
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