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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
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: 17 jun. 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 jun. 17 ] 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 jun. 17 ] 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
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ALGORITMOS
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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: 17 jun. 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 jun. 17 ] 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 jun. 17 ] 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
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: 17 jun. 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 jun. 17 ] 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 jun. 17 ] 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: 17 jun. 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 jun. 17 ] 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 jun. 17 ] 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
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: 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: 17 jun. 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 jun. 17 ] 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 jun. 17 ] Available from: https://doi.org/10.1007/s10618-020-00698-5
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
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: 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: 17 jun. 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 jun. 17 ] 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 jun. 17 ] Available from: https://doi.org/10.1007/s10618-018-0557-y
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
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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: 17 jun. 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 jun. 17 ] 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 jun. 17 ] Available from: https://doi.org/10.1007/s10618-015-0444-8
Artigo de periodico
MINAS: multiclass learning algorithm for novelty detection in data streams (2016)
Faria, Elaine Ribeiro de; Carvalho, André Carlos Ponce de Leon Ferreira de ; Gama, João
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FARIA, Elaine Ribeiro de e CARVALHO, André Carlos Ponce de Leon Ferreira de e GAMA, João. MINAS: multiclass learning algorithm for novelty detection in data streams. Data Mining and Knowledge Discovery, v. 30, n. 3, p. 640-680, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10618-015-0433-y. Acesso em: 17 jun. 2025.
APA
Faria, E. R. de, Carvalho, A. C. P. de L. F. de, & Gama, J. (2016). MINAS: multiclass learning algorithm for novelty detection in data streams. Data Mining and Knowledge Discovery, 30( 3), 640-680. doi:10.1007/s10618-015-0433-y
NLM
Faria ER de, Carvalho ACP de LF de, Gama J. MINAS: multiclass learning algorithm for novelty detection in data streams [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 3): 640-680.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-015-0433-y
Vancouver
Faria ER de, Carvalho ACP de LF de, Gama J. MINAS: multiclass learning algorithm for novelty detection in data streams [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 3): 640-680.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-015-0433-y
Artigo de periodico
Ensembles of label noise filters: a ranking approach (2016)
Garcia, Luís P. F; Lorena, Ana C; Matwin, Stan; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GARCIA, Luís P. F et al. Ensembles of label noise filters: a ranking approach. Data Mining and Knowledge Discovery, v. 30, n. 5, p. 1192-1216, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10618-016-0475-9. Acesso em: 17 jun. 2025.
APA
Garcia, L. P. F., Lorena, A. C., Matwin, S., & Carvalho, A. C. P. de L. F. de. (2016). Ensembles of label noise filters: a ranking approach. Data Mining and Knowledge Discovery, 30( 5), 1192-1216. doi:10.1007/s10618-016-0475-9
NLM
Garcia LPF, Lorena AC, Matwin S, Carvalho ACP de LF de. Ensembles of label noise filters: a ranking approach [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 5): 1192-1216.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-016-0475-9
Vancouver
Garcia LPF, Lorena AC, Matwin S, Carvalho ACP de LF de. Ensembles of label noise filters: a ranking approach [Internet]. Data Mining and Knowledge Discovery. 2016 ; 30( 5): 1192-1216.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-016-0475-9
Artigo de periodico
CID: an efficient complexity-invariant distance for time series (2014)
Batista, Gustavo Enrique de Almeida Prado Alves; Keogh, Eamonn J; Tataw, Oben Moses; Souza, Vinícius M. A. de
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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ABNT
BATISTA, Gustavo Enrique de Almeida Prado Alves et al. CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, v. 28, n. 3, p. 634-669, 2014Tradução . . Disponível em: https://doi.org/10.1007/s10618-013-0312-3. Acesso em: 17 jun. 2025.
APA
Batista, G. E. de A. P. A., Keogh, E. J., Tataw, O. M., & Souza, V. M. A. de. (2014). CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, 28( 3), 634-669. doi:10.1007/s10618-013-0312-3
NLM
Batista GE de APA, Keogh EJ, Tataw OM, Souza VMA de. CID: an efficient complexity-invariant distance for time series [Internet]. Data Mining and Knowledge Discovery. 2014 ; 28( 3): 634-669.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-013-0312-3
Vancouver
Batista GE de APA, Keogh EJ, Tataw OM, Souza VMA de. CID: an efficient complexity-invariant distance for time series [Internet]. Data Mining and Knowledge Discovery. 2014 ; 28( 3): 634-669.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-013-0312-3
Artigo de periodico
A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies (2013)
Campello, Ricardo José Gabrielli Barreto; Moulavi, D; Zimek, A; Sander, J
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CAMPELLO, Ricardo José Gabrielli Barreto et al. A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Mining and Knowledge Discovery, v. 27, n. 3, p. 344-371, 2013Tradução . . Disponível em: https://doi.org/10.1007/s10618-013-0311-4. Acesso em: 17 jun. 2025.
APA
Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2013). A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies. Data Mining and Knowledge Discovery, 27( 3), 344-371. doi:10.1007/s10618-013-0311-4
NLM
Campello RJGB, Moulavi D, Zimek A, Sander J. A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies [Internet]. Data Mining and Knowledge Discovery. 2013 ; 27( 3): 344-371.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-013-0311-4
Vancouver
Campello RJGB, Moulavi D, Zimek A, Sander J. A framework for semi-supervised and unsupervised optimal extraction of clusters from hierarchies [Internet]. Data Mining and Knowledge Discovery. 2013 ; 27( 3): 344-371.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-013-0311-4
Artigo de periodico
Cluster ensemble selection based on relative validity indexes (2013)
Naldi, M. C; Carvalho, André Carlos Ponce de Leon Ferreira de ; Campello, Ricardo José Gabrielli Barreto
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
NALDI, M. C e CARVALHO, André Carlos Ponce de Leon Ferreira de e CAMPELLO, Ricardo José Gabrielli Barreto. Cluster ensemble selection based on relative validity indexes. Data Mining and Knowledge Discovery, v. 27, n. 2, p. 259-289, 2013Tradução . . Disponível em: https://doi.org/10.1007/s10618-012-0290-x. Acesso em: 17 jun. 2025.
APA
Naldi, M. C., Carvalho, A. C. P. de L. F. de, & Campello, R. J. G. B. (2013). Cluster ensemble selection based on relative validity indexes. Data Mining and Knowledge Discovery, 27( 2), 259-289. doi:10.1007/s10618-012-0290-x
NLM
Naldi MC, Carvalho ACP de LF de, Campello RJGB. Cluster ensemble selection based on relative validity indexes [Internet]. Data Mining and Knowledge Discovery. 2013 ; 27( 2): 259-289.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-012-0290-x
Vancouver
Naldi MC, Carvalho ACP de LF de, Campello RJGB. Cluster ensemble selection based on relative validity indexes [Internet]. Data Mining and Knowledge Discovery. 2013 ; 27( 2): 259-289.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-012-0290-x
Artigo de periodico
A fast and effective method to find correlations among attributes in databases (2007)
Sousa, Elaine Parros Machado de ; Traina Junior, Caetano ; Traina, Agma Juci Machado ; Wu, Leejay; Faloutsos, Christos
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Subjects: COMPUTAÇÃO GRÁFICA, PROCESSAMENTO DE IMAGENS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SOUSA, Elaine Parros Machado de et al. A fast and effective method to find correlations among attributes in databases. Data Mining and Knowledge Discovery, v. 14, n. 3, p. 367-407, 2007Tradução . . Disponível em: https://doi.org/10.1007/s10618-006-0056-4. Acesso em: 17 jun. 2025.
APA
Sousa, E. P. M. de, Traina Junior, C., Traina, A. J. M., Wu, L., & Faloutsos, C. (2007). A fast and effective method to find correlations among attributes in databases. Data Mining and Knowledge Discovery, 14( 3), 367-407. doi:10.1007/s10618-006-0056-4
NLM
Sousa EPM de, Traina Junior C, Traina AJM, Wu L, Faloutsos C. A fast and effective method to find correlations among attributes in databases [Internet]. Data Mining and Knowledge Discovery. 2007 ; 14( 3): 367-407.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-006-0056-4
Vancouver
Sousa EPM de, Traina Junior C, Traina AJM, Wu L, Faloutsos C. A fast and effective method to find correlations among attributes in databases [Internet]. Data Mining and Knowledge Discovery. 2007 ; 14( 3): 367-407.[citado 2025 jun. 17 ] Available from: https://doi.org/10.1007/s10618-006-0056-4

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