Menu

Filtros : "Data Mining and Knowledge Discovery" "Sander, Jörg" Limpar

Filtros

Limitar por data

  • 1

1 - 3 (3 registros)

  • 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

    Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC

    Assuntos: BANCO DE DADOS, MINERAÇÃO DE DADOS

    Versão PublicadaAcesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • 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

    A unified view of density-based methods for semi-supervised clustering and classification (2019)

    Gertrudes, Jadson Castro ; Zimek, Arthur ; Sander, Jörg ; Campello, Ricardo José Gabrielli Barreto

    Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC

    Assuntos: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS

    Versão PublicadaAcesso à fonteAcesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      GERTRUDES, Jadson Castro et al. A unified view of density-based methods for semi-supervised clustering and classification. Data Mining and Knowledge Discovery, v. No 2019, n. 6, p. 1894-1952, 2019Tradução . . Disponível em: https://doi.org/10.1007/s10618-019-00651-1. Acesso em: 11 nov. 2025.

    • APA

      Gertrudes, J. C., Zimek, A., Sander, J., & Campello, R. J. G. B. (2019). A unified view of density-based methods for semi-supervised clustering and classification. Data Mining and Knowledge Discovery, No 2019( 6), 1894-1952. doi:10.1007/s10618-019-00651-1

    • NLM

      Gertrudes JC, Zimek A, Sander J, Campello RJGB. A unified view of density-based methods for semi-supervised clustering and classification [Internet]. Data Mining and Knowledge Discovery. 2019 ; No 2019( 6): 1894-1952.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-019-00651-1

    • Vancouver

      Gertrudes JC, Zimek A, Sander J, Campello RJGB. A unified view of density-based methods for semi-supervised clustering and classification [Internet]. Data Mining and Knowledge Discovery. 2019 ; No 2019( 6): 1894-1952.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-019-00651-1

  • Artigo de periodico

    On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study (2016)

    Campos, Guilherme O; Zimek, Arthur; Sander, Jörg; Campello, Ricardo José Gabrielli Barreto; Micenková, Barbora; Schubert, Erich; Assent, Ira; Houle, Michael E

    Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

    Acesso à fonteDOIComo citar
    A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas

    • ABNT

      CAMPOS, Guilherme O et al. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, v. 30, n. 4, p. 891-927, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10618-015-0444-8. Acesso em: 11 nov. 2025.

    • APA

      Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenková, B., Schubert, E., et al. (2016). On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery, 30( 4), 891-927. doi:10.1007/s10618-015-0444-8

    • NLM

      Campos GO, Zimek A, Sander J, Campello RJGB, Micenková B, Schubert E, Assent I, Houle ME. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 4): 891-927.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-015-0444-8

    • Vancouver

      Campos GO, Zimek A, Sander J, Campello RJGB, Micenková B, Schubert E, Assent I, Houle ME. On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 4): 891-927.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-015-0444-8
  • 1

1 - 3 (3 registros)

Biblioteca Digital de Produção Intelectual da Universidade de São Paulo     2012 - 2025