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Artigo de periodico
A comparative evaluation of clustering-based outlier detection (2025)
Sánchez Vinces, Braulio Valentin ; Schubert, Erich ; Zimek, Arthur ; Cordeiro, Robson Leonardo Ferreira
Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS, DESCOBERTA DE CONHECIMENTO, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SÁNCHEZ VINCES, Braulio Valentin et al. A comparative evaluation of clustering-based outlier detection. Data Mining and Knowledge Discovery, v. 39, n. 2, p. 1-55, 2025Tradução . . Disponível em: https://doi.org/10.1007/s10618-024-01086-z. Acesso em: 11 nov. 2025.
APA
Sánchez Vinces, B. V., Schubert, E., Zimek, A., & Cordeiro, R. L. F. (2025). A comparative evaluation of clustering-based outlier detection. Data Mining and Knowledge Discovery, 39( 2), 1-55. doi:10.1007/s10618-024-01086-z
NLM
Sánchez Vinces BV, Schubert E, Zimek A, Cordeiro RLF. A comparative evaluation of clustering-based outlier detection [Internet]. Data Mining and Knowledge Discovery. 2025 ; 39( 2): 1-55.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-024-01086-z
Vancouver
Sánchez Vinces BV, Schubert E, Zimek A, Cordeiro RLF. A comparative evaluation of clustering-based outlier detection [Internet]. Data Mining and Knowledge Discovery. 2025 ; 39( 2): 1-55.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-024-01086-z
Artigo de periodico
ARM-stream: active recovery of miscategorizations in clustering-based data stream classifiers (2025)
Cavalcanti, Douglas Monteiro; Cerri, Ricardo ; Faria, Elaine Ribeiro
Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, FRAMEWORKS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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
Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC
Assuntos: BANCO DE DADOS, MINERAÇÃO DE DADOS
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
Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm (2024)
Raimundo, Marcos M ; Nonato, Luis Gustavo ; Poco, Jorge
Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, ALGORITMOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RAIMUNDO, Marcos M e NONATO, Luis Gustavo e POCO, Jorge. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery, v. 38, p. 2942-2974, 2024Tradução . . Disponível em: https://doi.org/10.1007/s10618-022-00906-4. Acesso em: 11 nov. 2025.
APA
Raimundo, M. M., Nonato, L. G., & Poco, J. (2024). Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm. Data Mining and Knowledge Discovery, 38, 2942-2974. doi:10.1007/s10618-022-00906-4
NLM
Raimundo MM, Nonato LG, Poco J. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38 2942-2974.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-022-00906-4
Vancouver
Raimundo MM, Nonato LG, Poco J. Mining Pareto-optimal counterfactual antecedents with a branch-and-boundmodel-agnostic algorithm [Internet]. Data Mining and Knowledge Discovery. 2024 ; 38 2942-2974.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-022-00906-4
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
Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, ALGORITMOS ÚTEIS E ESPECÍFICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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
Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC
Assuntos: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, ALGORITMOS ÚTEIS E ESPECÍFICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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
Artigo de periodico
Challenges in benchmarking stream learning algorithms with real-world data (2020)
Souza, Vinícius Mourão Alves de ; Reis, Denis Moreira dos ; Maletzke, André Gustavo ; Batista, Gustavo Enrique de Almeida Prado Alves
Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC
Assuntos: ANÁLISE DE SÉRIES TEMPORAIS, MINERAÇÃO DE DADOS, ALGORITMOS ÚTEIS E ESPECÍFICOS, BENCHMARKS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUZA, Vinícius Mourão Alves de et al. Challenges in benchmarking stream learning algorithms with real-world data. Data Mining and Knowledge Discovery, v. No 2020, n. 6, p. 1805-1858, 2020Tradução . . Disponível em: https://doi.org/10.1007/s10618-020-00698-5. Acesso em: 11 nov. 2025.
APA
Souza, V. M. A. de, Reis, D. M. dos, Maletzke, A. G., & Batista, G. E. de A. P. A. (2020). Challenges in benchmarking stream learning algorithms with real-world data. Data Mining and Knowledge Discovery, No 2020( 6), 1805-1858. doi:10.1007/s10618-020-00698-5
NLM
Souza VMA de, Reis DM dos, Maletzke AG, Batista GE de APA. Challenges in benchmarking stream learning algorithms with real-world data [Internet]. Data Mining and Knowledge Discovery. 2020 ; No 2020( 6): 1805-1858.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-020-00698-5
Vancouver
Souza VMA de, Reis DM dos, Maletzke AG, Batista GE de APA. Challenges in benchmarking stream learning algorithms with real-world data [Internet]. Data Mining and Knowledge Discovery. 2020 ; No 2020( 6): 1805-1858.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-020-00698-5
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
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
Speeding up similarity search under dynamic time warping by pruning unpromising alignments (2018)
Silva, Diego Furtado ; Giusti, Rafael; Keogh, Eamonn; Batista, Gustavo Enrique de Almeida Prado Alves
Fonte: Data Mining and Knowledge Discovery. Unidade: ICMC
Assuntos: MINERAÇÃO DE DADOS, ANÁLISE DE SÉRIES TEMPORAIS, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SILVA, Diego Furtado et al. Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Mining and Knowledge Discovery, v. 32, n. 4, p. 988-1016, 2018Tradução . . Disponível em: https://doi.org/10.1007/s10618-018-0557-y. Acesso em: 11 nov. 2025.
APA
Silva, D. F., Giusti, R., Keogh, E., & Batista, G. E. de A. P. A. (2018). Speeding up similarity search under dynamic time warping by pruning unpromising alignments. Data Mining and Knowledge Discovery, 32( 4), 988-1016. doi:10.1007/s10618-018-0557-y
NLM
Silva DF, Giusti R, Keogh E, Batista GE de APA. Speeding up similarity search under dynamic time warping by pruning unpromising alignments [Internet]. Data Mining and Knowledge Discovery. 2018 ; 32( 4): 988-1016.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-018-0557-y
Vancouver
Silva DF, Giusti R, Keogh E, Batista GE de APA. Speeding up similarity search under dynamic time warping by pruning unpromising alignments [Internet]. Data Mining and Knowledge Discovery. 2018 ; 32( 4): 988-1016.[citado 2025 nov. 11 ] Available from: https://doi.org/10.1007/s10618-018-0557-y

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