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  • Source: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC

    Subjects: MINERAÇÃO DE DADOS, APRENDIZADO COMPUTACIONAL

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    • ABNT

      MARQUES, Henrique Oliveira et al. Internal evaluation of unsupervised outlier detection. ACM Transactions on Knowledge Discovery from Data, v. 14, n. 4, p. 47:1-47:42, 2020Tradução . . Disponível em: https://doi.org/10.1145/3394053. Acesso em: 28 nov. 2025.
    • APA

      Marques, H. O., Campello, R. J. G. B., Sander, J., & Zimek, A. (2020). Internal evaluation of unsupervised outlier detection. ACM Transactions on Knowledge Discovery from Data, 14( 4), 47:1-47:42. doi:10.1145/3394053
    • NLM

      Marques HO, Campello RJGB, Sander J, Zimek A. Internal evaluation of unsupervised outlier detection [Internet]. ACM Transactions on Knowledge Discovery from Data. 2020 ; 14( 4): 47:1-47:42.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1145/3394053
    • Vancouver

      Marques HO, Campello RJGB, Sander J, Zimek A. Internal evaluation of unsupervised outlier detection [Internet]. ACM Transactions on Knowledge Discovery from Data. 2020 ; 14( 4): 47:1-47:42.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1145/3394053
  • Source: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC

    Subjects: BANCO DE DADOS, MINERAÇÃO DE DADOS, MÍDIAS SOCIAIS

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    • ABNT

      COSTA, Alceu Ferraz et al. Modeling temporal activity to detect anomalous behavior in social media. ACM Transactions on Knowledge Discovery from Data, v. 11, n. 4. p. 49:1-49:23, 2017Tradução . . Disponível em: https://doi.org/10.1145/3064884. Acesso em: 28 nov. 2025.
    • APA

      Costa, A. F., Yamaguchi, Y., Traina, A. J. M., Traina Junior, C., & Faloutsos, C. (2017). Modeling temporal activity to detect anomalous behavior in social media. ACM Transactions on Knowledge Discovery from Data, 11( 4. p. 49:1-49:23). doi:10.1145/3064884
    • NLM

      Costa AF, Yamaguchi Y, Traina AJM, Traina Junior C, Faloutsos C. Modeling temporal activity to detect anomalous behavior in social media [Internet]. ACM Transactions on Knowledge Discovery from Data. 2017 ; 11( 4. p. 49:1-49:23):[citado 2025 nov. 28 ] Available from: https://doi.org/10.1145/3064884
    • Vancouver

      Costa AF, Yamaguchi Y, Traina AJM, Traina Junior C, Faloutsos C. Modeling temporal activity to detect anomalous behavior in social media [Internet]. ACM Transactions on Knowledge Discovery from Data. 2017 ; 11( 4. p. 49:1-49:23):[citado 2025 nov. 28 ] Available from: https://doi.org/10.1145/3064884
  • Source: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, MINERAÇÃO DE DADOS, RECUPERAÇÃO DA INFORMAÇÃO, RECONHECIMENTO DE PADRÕES

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    • ABNT

      CAMPELLO, Ricardo José Gabrielli Barreto et al. Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data, v. 10, n. 1, p. 5:1-5:51, 2015Tradução . . Disponível em: https://doi.org/10.1145/2733381. Acesso em: 28 nov. 2025.
    • APA

      Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical density estimates for data clustering, visualization, and outlier detection. ACM Transactions on Knowledge Discovery from Data, 10( 1), 5:1-5:51. doi:10.1145/2733381
    • NLM

      Campello RJGB, Moulavi D, Zimek A, Sander J. Hierarchical density estimates for data clustering, visualization, and outlier detection [Internet]. ACM Transactions on Knowledge Discovery from Data. 2015 ; 10( 1): 5:1-5:51.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1145/2733381
    • Vancouver

      Campello RJGB, Moulavi D, Zimek A, Sander J. Hierarchical density estimates for data clustering, visualization, and outlier detection [Internet]. ACM Transactions on Knowledge Discovery from Data. 2015 ; 10( 1): 5:1-5:51.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1145/2733381
  • Source: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC

    Subjects: INTELIGÊNCIA ARTIFICIAL, RECONHECIMENTO DE PADRÕES, ALGORITMOS, VISÃO COMPUTACIONAL

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    • ABNT

      ACHARYA, Ayan et al. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning. ACM Transactions on Knowledge Discovery from Data, v. 9, n. 1, p. 1:1-1:35, 2014Tradução . . Disponível em: https://doi.org/10.1145/2601435. Acesso em: 28 nov. 2025.
    • APA

      Acharya, A., Hruschka, E. R., Ghosh, J., & Acharyya, S. (2014). An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning. ACM Transactions on Knowledge Discovery from Data, 9( 1), 1:1-1:35. doi:10.1145/2601435
    • NLM

      Acharya A, Hruschka ER, Ghosh J, Acharyya S. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning [Internet]. ACM Transactions on Knowledge Discovery from Data. 2014 ; 9( 1): 1:1-1:35.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1145/2601435
    • Vancouver

      Acharya A, Hruschka ER, Ghosh J, Acharyya S. An optimization framework for combining ensembles of classifiers and clusterers with applications to nontransductive semisupervised learning and transfer learning [Internet]. ACM Transactions on Knowledge Discovery from Data. 2014 ; 9( 1): 1:1-1:35.[citado 2025 nov. 28 ] Available from: https://doi.org/10.1145/2601435

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