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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: 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
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: 11 nov. 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 nov. 11 ] 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 nov. 11 ] 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: 11 nov. 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 nov. 11 ] 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 nov. 11 ] 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
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 11 nov. 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 nov. 11 ] 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 nov. 11 ] 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: 11 nov. 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 nov. 11 ] 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 nov. 11 ] 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: 11 nov. 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 nov. 11 ] 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 nov. 11 ] Available from: https://doi.org/10.1007/s10618-012-0290-x

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