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Monografia/livro-ed/org
[Book of abstracts] (2019)
Arrieta, José María (Organizador); Caraballo, Tomás (Organizador); Carvalho, Alexandre Nolasco de (Organizador) ; Chow, Shui-Nee (Organizador); Figueiredo, Djairo Guedes de (Organizador); Mallet-Paret, John (Organizador); Rodrigues, Hildebrando Munhoz (Organizador) ; Yi, Yingfei (Organizador)
Conference titles: ICMC Summer Meeting on Differential Equations. Unidade: ICMC
Assunto: EQUAÇÕES DIFERENCIAIS
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ABNT
[Book of abstracts]. . São Carlos: ICMC-USP. Disponível em: http://summer.icmc.usp.br/summers/summer19/pg_abstract.php. Acesso em: 10 nov. 2024. , 2019
APA
[Book of abstracts]. (2019). [Book of abstracts]. São Carlos: ICMC-USP. Recuperado de http://summer.icmc.usp.br/summers/summer19/pg_abstract.php
NLM
[Book of abstracts] [Internet]. 2019 ;[citado 2024 nov. 10 ] Available from: http://summer.icmc.usp.br/summers/summer19/pg_abstract.php
Vancouver
[Book of abstracts] [Internet]. 2019 ;[citado 2024 nov. 10 ] Available from: http://summer.icmc.usp.br/summers/summer19/pg_abstract.php
Trabalho de evento
A unified framework of density-based clustering for semi-supervised classification (2018)
Gertrudes, Jadson Castro; Zimek, Arthur; Sander, Jörg; Campello, Ricardo José Gabrielli Barreto
Source: Proceedings. Conference titles: International Conference on Scientific and Statistical Database Management - SSDBM. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, ALGORITMOS ÚTEIS E ESPECÍFICOS
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ABNT
GERTRUDES, Jadson Castro et al. A unified framework of density-based clustering for semi-supervised classification. 2018, Anais.. New York: ACM, 2018. Disponível em: https://doi.org/10.1145/3221269.3223037. Acesso em: 10 nov. 2024.
APA
Gertrudes, J. C., Zimek, A., Sander, J., & Campello, R. J. G. B. (2018). A unified framework of density-based clustering for semi-supervised classification. In Proceedings. New York: ACM. doi:10.1145/3221269.3223037
NLM
Gertrudes JC, Zimek A, Sander J, Campello RJGB. A unified framework of density-based clustering for semi-supervised classification [Internet]. Proceedings. 2018 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1145/3221269.3223037
Vancouver
Gertrudes JC, Zimek A, Sander J, Campello RJGB. A unified framework of density-based clustering for semi-supervised classification [Internet]. Proceedings. 2018 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1145/3221269.3223037
Trabalho de evento
Efficient computation of multiple density-based clustering hierarchies (2017)
Araujo Neto, Antonio Cavalcante; Sander, Joerg; Campello, Ricardo José Gabrielli Barreto; Nascimento, Mario A
Source: Proceedings. Conference titles: IEEE International Conference on Data Mining - ICDM. Unidade: ICMC
Subjects: RECONHECIMENTO DE PADRÕES, MINERAÇÃO DE DADOS, DESCOBERTA DE CONHECIMENTO
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ABNT
ARAUJO NETO, Antonio Cavalcante et al. Efficient computation of multiple density-based clustering hierarchies. 2017, Anais.. Piscataway: IEEE, 2017. Disponível em: https://doi.org/10.1109/ICDM.2017.127. Acesso em: 10 nov. 2024.
APA
Araujo Neto, A. C., Sander, J., Campello, R. J. G. B., & Nascimento, M. A. (2017). Efficient computation of multiple density-based clustering hierarchies. In Proceedings. Piscataway: IEEE. doi:10.1109/ICDM.2017.127
NLM
Araujo Neto AC, Sander J, Campello RJGB, Nascimento MA. Efficient computation of multiple density-based clustering hierarchies [Internet]. Proceedings. 2017 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1109/ICDM.2017.127
Vancouver
Araujo Neto AC, Sander J, Campello RJGB, Nascimento MA. Efficient computation of multiple density-based clustering hierarchies [Internet]. Proceedings. 2017 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1109/ICDM.2017.127
Parte de monografia/livro
Relative validity criteria for community mining algorithms (2017)
Rabbany, Reihaneh; Takaffoli, Mansoreh; Fagnan, Justin; Zaïane, Osmar R; Campello, Ricardo José Gabrielli Barreto
Source: Encyclopedia of social network analysis and mining. Unidade: ICMC
Subjects: MINERAÇÃO DE DADOS, DESCOBERTA DE CONHECIMENTO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RABBANY, Reihaneh et al. Relative validity criteria for community mining algorithms. Encyclopedia of social network analysis and mining. Tradução . New York: Springer, 2017. . Disponível em: https://doi.org/10.1007/978-1-4614-7163-9_356-1. Acesso em: 10 nov. 2024.
APA
Rabbany, R., Takaffoli, M., Fagnan, J., Zaïane, O. R., & Campello, R. J. G. B. (2017). Relative validity criteria for community mining algorithms. In Encyclopedia of social network analysis and mining. New York: Springer. doi:10.1007/978-1-4614-7163-9_356-1
NLM
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB. Relative validity criteria for community mining algorithms [Internet]. In: Encyclopedia of social network analysis and mining. New York: Springer; 2017. [citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/978-1-4614-7163-9_356-1
Vancouver
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB. Relative validity criteria for community mining algorithms [Internet]. In: Encyclopedia of social network analysis and mining. New York: Springer; 2017. [citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/978-1-4614-7163-9_356-1
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
Source: Data Mining and Knowledge Discovery. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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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: 10 nov. 2024.
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 2024 nov. 10 ] 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 2024 nov. 10 ] Available from: https://doi.org/10.1007/s10618-015-0444-8
Trabalho de evento
On the evaluation of outlier detection and one-class classification methods (2016)
Swersky, Lorne; Marques, Henrique O; Sander, Jörg; Campello, Ricardo José Gabrielli Barreto; Zimek, Arthur
Source: Proceedings. Conference titles: IEEE International Conference on Data Science and Advanced Analytics - DSAA. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES
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ABNT
SWERSKY, Lorne et al. On the evaluation of outlier detection and one-class classification methods. 2016, Anais.. Los Alamitos: IEEE, 2016. Disponível em: https://doi.org/10.1109/DSAA.2016.8. Acesso em: 10 nov. 2024.
APA
Swersky, L., Marques, H. O., Sander, J., Campello, R. J. G. B., & Zimek, A. (2016). On the evaluation of outlier detection and one-class classification methods. In Proceedings. Los Alamitos: IEEE. doi:10.1109/DSAA.2016.8
NLM
Swersky L, Marques HO, Sander J, Campello RJGB, Zimek A. On the evaluation of outlier detection and one-class classification methods [Internet]. Proceedings. 2016 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1109/DSAA.2016.8
Vancouver
Swersky L, Marques HO, Sander J, Campello RJGB, Zimek A. On the evaluation of outlier detection and one-class classification methods [Internet]. Proceedings. 2016 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1109/DSAA.2016.8
Trabalho de evento
Active semi-supervised classification based on multiple clustering hierarchies (2016)
Batista, Antônio J. L; Campello, Ricardo José Gabrielli Barreto; Sander, Jörg
Source: Proceedings. Conference titles: IEEE International Conference on Data Science and Advanced Analytics - DSAA. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BATISTA, Antônio J. L e CAMPELLO, Ricardo José Gabrielli Barreto e SANDER, Jörg. Active semi-supervised classification based on multiple clustering hierarchies. 2016, Anais.. Los Alamitos: IEEE, 2016. Disponível em: https://doi.org/10.1109/DSAA.2016.9. Acesso em: 10 nov. 2024.
APA
Batista, A. J. L., Campello, R. J. G. B., & Sander, J. (2016). Active semi-supervised classification based on multiple clustering hierarchies. In Proceedings. Los Alamitos: IEEE. doi:10.1109/DSAA.2016.9
NLM
Batista AJL, Campello RJGB, Sander J. Active semi-supervised classification based on multiple clustering hierarchies [Internet]. Proceedings. 2016 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1109/DSAA.2016.9
Vancouver
Batista AJL, Campello RJGB, Sander J. Active semi-supervised classification based on multiple clustering hierarchies [Internet]. Proceedings. 2016 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1109/DSAA.2016.9
Artigo de periodico
On strategies for building effective ensembles of relative clustering validity criteria (2016)
Jaskowiak, Pablo A; Moulavi, Davoud; Furtado, Antonio C. S; Campello, Ricardo José Gabrielli Barreto; Zimek, Arthur; Sander, Jörg
Source: Knowledge and Information Systems. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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ABNT
JASKOWIAK, Pablo A et al. On strategies for building effective ensembles of relative clustering validity criteria. Knowledge and Information Systems, v. 47, n. 2, p. 329-354, 2016Tradução . . Disponível em: https://doi.org/10.1007/s10115-015-0851-6. Acesso em: 10 nov. 2024.
APA
Jaskowiak, P. A., Moulavi, D., Furtado, A. C. S., Campello, R. J. G. B., Zimek, A., & Sander, J. (2016). On strategies for building effective ensembles of relative clustering validity criteria. Knowledge and Information Systems, 47( 2), 329-354. doi:10.1007/s10115-015-0851-6
NLM
Jaskowiak PA, Moulavi D, Furtado ACS, Campello RJGB, Zimek A, Sander J. On strategies for building effective ensembles of relative clustering validity criteria [Internet]. Knowledge and Information Systems. 2016 ; 47( 2): 329-354.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/s10115-015-0851-6
Vancouver
Jaskowiak PA, Moulavi D, Furtado ACS, Campello RJGB, Zimek A, Sander J. On strategies for building effective ensembles of relative clustering validity criteria [Internet]. Knowledge and Information Systems. 2016 ; 47( 2): 329-354.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/s10115-015-0851-6
Artigo de periodico
Hierarchical density estimates for data clustering, visualization, and outlier detection (2015)
Campello, Ricardo José Gabrielli Barreto; Moulavi, Davoud; Zimek, Arthur; Sander, Jörg
Source: ACM Transactions on Knowledge Discovery from Data. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, MINERAÇÃO DE DADOS, RECUPERAÇÃO DA INFORMAÇÃO, RECONHECIMENTO DE PADRÕES
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 10 nov. 2024.
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 2024 nov. 10 ] 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 2024 nov. 10 ] Available from: https://doi.org/10.1145/2733381
Trabalho de evento
On the internal evaluation of unsupervised outlier detection (2015)
Marques, Henrique O; Campello, Ricardo José Gabrielli Barreto; Zimek, Arthur; Sander, Jörg
Source: Proceedings. Conference titles: International Conference on Scientific and Statistical Database Management - SSDBM. Unidade: ICMC
Subjects: INTELIGÊNCIA ARTIFICIAL, MINERAÇÃO DE DADOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MARQUES, Henrique O et al. On the internal evaluation of unsupervised outlier detection. 2015, Anais.. New York: ACM, 2015. Disponível em: https://doi.org/10.1145/2791347.2791352. Acesso em: 10 nov. 2024.
APA
Marques, H. O., Campello, R. J. G. B., Zimek, A., & Sander, J. (2015). On the internal evaluation of unsupervised outlier detection. In Proceedings. New York: ACM. doi:10.1145/2791347.2791352
NLM
Marques HO, Campello RJGB, Zimek A, Sander J. On the internal evaluation of unsupervised outlier detection [Internet]. Proceedings. 2015 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1145/2791347.2791352
Vancouver
Marques HO, Campello RJGB, Zimek A, Sander J. On the internal evaluation of unsupervised outlier detection [Internet]. Proceedings. 2015 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1145/2791347.2791352
Trabalho de evento
Model selection for semi-supervised clustering (2014)
Pourrajabi, Mojgan; Moulavi, Davoud; Campello, Ricardo José Gabrielli Barreto; Zimek, Arthur; Sander, Jörg; Goebel, Randy
Source: Proceedings. Conference titles: International Conference on Extending Database Technology - EDBT. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
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ABNT
POURRAJABI, Mojgan et al. Model selection for semi-supervised clustering. 2014, Anais.. Konstanz: OpenProceedings, 2014. Disponível em: https://doi.org/10.5441/002/edbt.2014.31. Acesso em: 10 nov. 2024.
APA
Pourrajabi, M., Moulavi, D., Campello, R. J. G. B., Zimek, A., Sander, J., & Goebel, R. (2014). Model selection for semi-supervised clustering. In Proceedings. Konstanz: OpenProceedings. doi:10.5441/002/edbt.2014.31
NLM
Pourrajabi M, Moulavi D, Campello RJGB, Zimek A, Sander J, Goebel R. Model selection for semi-supervised clustering [Internet]. Proceedings. 2014 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.5441/002/edbt.2014.31
Vancouver
Pourrajabi M, Moulavi D, Campello RJGB, Zimek A, Sander J, Goebel R. Model selection for semi-supervised clustering [Internet]. Proceedings. 2014 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.5441/002/edbt.2014.31
Trabalho de evento
Data perturbation for outlier detection ensembles (2014)
Zimek, Arthur; Campello, Ricardo José Gabrielli Barreto; Sander, Jörg
Source: Proceedings. Conference titles: International Conference on Scientific and Statistical Database Management - SSDBM. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ZIMEK, Arthur e CAMPELLO, Ricardo José Gabrielli Barreto e SANDER, Jörg. Data perturbation for outlier detection ensembles. 2014, Anais.. New York: ACM, 2014. Disponível em: https://doi.org/10.1145/2618243.2618257. Acesso em: 10 nov. 2024.
APA
Zimek, A., Campello, R. J. G. B., & Sander, J. (2014). Data perturbation for outlier detection ensembles. In Proceedings. New York: ACM. doi:10.1145/2618243.2618257
NLM
Zimek A, Campello RJGB, Sander J. Data perturbation for outlier detection ensembles [Internet]. Proceedings. 2014 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1145/2618243.2618257
Vancouver
Zimek A, Campello RJGB, Sander J. Data perturbation for outlier detection ensembles [Internet]. Proceedings. 2014 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1145/2618243.2618257
Parte de monografia/livro
Relative validity criteria for community mining algorithms (2014)
Rabbany, Reihaneh; Takaffoli, Mansoreh; Fagnan, Justin; Zaïane, Osmar R; Campello, Ricardo José Gabrielli Barreto
Source: Encyclopedia of social network analysis and mining. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RABBANY, Reihaneh et al. Relative validity criteria for community mining algorithms. Encyclopedia of social network analysis and mining. Tradução . New York: Springer, 2014. . Disponível em: https://doi.org/10.1007/978-1-4614-6170-8_356. Acesso em: 10 nov. 2024.
APA
Rabbany, R., Takaffoli, M., Fagnan, J., Zaïane, O. R., & Campello, R. J. G. B. (2014). Relative validity criteria for community mining algorithms. In Encyclopedia of social network analysis and mining. New York: Springer. doi:10.1007/978-1-4614-6170-8_356
NLM
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB. Relative validity criteria for community mining algorithms [Internet]. In: Encyclopedia of social network analysis and mining. New York: Springer; 2014. [citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/978-1-4614-6170-8_356
Vancouver
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB. Relative validity criteria for community mining algorithms [Internet]. In: Encyclopedia of social network analysis and mining. New York: Springer; 2014. [citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/978-1-4614-6170-8_356
Trabalho de evento
Density-based clustering validation (2014)
Moulavi, Davoud; Jaskowiak, Pablo A; Campello, Ricardo José Gabrielli Barreto; Zimek, Arthur; Sander, Jörg
Source: Proceedings. Conference titles: SIAM International Conference on Data Mining - SDM. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MOULAVI, Davoud et al. Density-based clustering validation. 2014, Anais.. Philadelphia: SIAM, 2014. . Acesso em: 10 nov. 2024.
APA
Moulavi, D., Jaskowiak, P. A., Campello, R. J. G. B., Zimek, A., & Sander, J. (2014). Density-based clustering validation. In Proceedings. Philadelphia: SIAM.
NLM
Moulavi D, Jaskowiak PA, Campello RJGB, Zimek A, Sander J. Density-based clustering validation. Proceedings. 2014 ;[citado 2024 nov. 10 ]
Vancouver
Moulavi D, Jaskowiak PA, Campello RJGB, Zimek A, Sander J. Density-based clustering validation. Proceedings. 2014 ;[citado 2024 nov. 10 ]
Trabalho de evento-anais periodico
Active learning strategies for semi-supervised DBSCAN (2014)
Li, Jundong; Sander, Jörg; Campello, Ricardo José Gabrielli Barreto; Zimek, Arthur
Source: Lecture Notes in Artificial Intelligence. Conference titles: Canadian Conference on Artificial Intelligence : Advances in Artificial Intelligence - Canadian AI. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LI, Jundong et al. Active learning strategies for semi-supervised DBSCAN. Lecture Notes in Artificial Intelligence. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-319-06483-3_16. Acesso em: 10 nov. 2024. , 2014
APA
Li, J., Sander, J., Campello, R. J. G. B., & Zimek, A. (2014). Active learning strategies for semi-supervised DBSCAN. Lecture Notes in Artificial Intelligence. Cham: Springer. doi:10.1007/978-3-319-06483-3_16
NLM
Li J, Sander J, Campello RJGB, Zimek A. Active learning strategies for semi-supervised DBSCAN [Internet]. Lecture Notes in Artificial Intelligence. 2014 ; 8436 179-190.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/978-3-319-06483-3_16
Vancouver
Li J, Sander J, Campello RJGB, Zimek A. Active learning strategies for semi-supervised DBSCAN [Internet]. Lecture Notes in Artificial Intelligence. 2014 ; 8436 179-190.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/978-3-319-06483-3_16
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: 10 nov. 2024.
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 2024 nov. 10 ] 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 2024 nov. 10 ] Available from: https://doi.org/10.1007/s10618-013-0311-4
Trabalho de evento
Subsampling for efficient and effective unsupervised outlier detection ensembles (2013)
Zimek, Arthur; Gaudet, Matthew; Campello, Ricardo José Gabrielli Barreto; Sander, Jörg
Source: Proceedings. Conference titles: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ZIMEK, Arthur et al. Subsampling for efficient and effective unsupervised outlier detection ensembles. 2013, Anais.. New York: ACM, 2013. Disponível em: https://doi.org/10.1145/2487575.2487676. Acesso em: 10 nov. 2024.
APA
Zimek, A., Gaudet, M., Campello, R. J. G. B., & Sander, J. (2013). Subsampling for efficient and effective unsupervised outlier detection ensembles. In Proceedings. New York: ACM. doi:10.1145/2487575.2487676
NLM
Zimek A, Gaudet M, Campello RJGB, Sander J. Subsampling for efficient and effective unsupervised outlier detection ensembles [Internet]. Proceedings. 2013 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1145/2487575.2487676
Vancouver
Zimek A, Gaudet M, Campello RJGB, Sander J. Subsampling for efficient and effective unsupervised outlier detection ensembles [Internet]. Proceedings. 2013 ;[citado 2024 nov. 10 ] Available from: https://doi.org/10.1145/2487575.2487676
Artigo de periodico
Communities validity: methodical evaluation of community mining algorithms (2013)
Rabbany, Reihaneh; Takaffoli, Mansoureh; Fagnan, Justin; Zaïane, Osmar R; Campello, Ricardo José Gabrielli Barreto
Source: Social Network Analysis and Mining. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RABBANY, Reihaneh et al. Communities validity: methodical evaluation of community mining algorithms. Social Network Analysis and Mining, v. 3, n. 4, p. 1039-1062, 2013Tradução . . Disponível em: https://doi.org/10.1007/s13278-013-0132-x. Acesso em: 10 nov. 2024.
APA
Rabbany, R., Takaffoli, M., Fagnan, J., Zaïane, O. R., & Campello, R. J. G. B. (2013). Communities validity: methodical evaluation of community mining algorithms. Social Network Analysis and Mining, 3( 4), 1039-1062. doi:10.1007/s13278-013-0132-x
NLM
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB. Communities validity: methodical evaluation of community mining algorithms [Internet]. Social Network Analysis and Mining. 2013 ; 3( 4): 1039-1062.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/s13278-013-0132-x
Vancouver
Rabbany R, Takaffoli M, Fagnan J, Zaïane OR, Campello RJGB. Communities validity: methodical evaluation of community mining algorithms [Internet]. Social Network Analysis and Mining. 2013 ; 3( 4): 1039-1062.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/s13278-013-0132-x
Artigo de periodico
Ensembles for unsupervised outlier detection: challenges and research questions (2013)
Zimek, Arthur; Campello, Ricardo José Gabrielli Barreto; Sander, Jörg
Source: SIGKDD Explorations. Unidade: ICMC
Assunto: INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
ZIMEK, Arthur e CAMPELLO, Ricardo José Gabrielli Barreto e SANDER, Jörg. Ensembles for unsupervised outlier detection: challenges and research questions. SIGKDD Explorations, v. 15, n. ju 2013, p. 11-22, 2013Tradução . . Disponível em: http://www.kdd.org/newsletter/explorations-june-2013-15-1. Acesso em: 10 nov. 2024.
APA
Zimek, A., Campello, R. J. G. B., & Sander, J. (2013). Ensembles for unsupervised outlier detection: challenges and research questions. SIGKDD Explorations, 15( ju 2013), 11-22. Recuperado de http://www.kdd.org/newsletter/explorations-june-2013-15-1
NLM
Zimek A, Campello RJGB, Sander J. Ensembles for unsupervised outlier detection: challenges and research questions [Internet]. SIGKDD Explorations. 2013 ; 15( ju 2013): 11-22.[citado 2024 nov. 10 ] Available from: http://www.kdd.org/newsletter/explorations-june-2013-15-1
Vancouver
Zimek A, Campello RJGB, Sander J. Ensembles for unsupervised outlier detection: challenges and research questions [Internet]. SIGKDD Explorations. 2013 ; 15( ju 2013): 11-22.[citado 2024 nov. 10 ] Available from: http://www.kdd.org/newsletter/explorations-june-2013-15-1
Trabalho de evento-anais periodico
Density-based clustering based on hierarchical density estimates (2013)
Campello, Ricardo José Gabrielli Barreto; Moulavi, Davoud; Sander, Joerg
Source: Lecture Notes in Artificial Intelligence. Conference titles: Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining - PAKDD. 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 e MOULAVI, Davoud e SANDER, Joerg. Density-based clustering based on hierarchical density estimates. Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag. Disponível em: https://doi.org/10.1007/978-3-642-37456-2. Acesso em: 10 nov. 2024. , 2013
APA
Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-based clustering based on hierarchical density estimates. Lecture Notes in Artificial Intelligence. Berlin: Springer-Verlag. doi:10.1007/978-3-642-37456-2
NLM
Campello RJGB, Moulavi D, Sander J. Density-based clustering based on hierarchical density estimates [Internet]. Lecture Notes in Artificial Intelligence. 2013 ; 7819 160-172.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/978-3-642-37456-2
Vancouver
Campello RJGB, Moulavi D, Sander J. Density-based clustering based on hierarchical density estimates [Internet]. Lecture Notes in Artificial Intelligence. 2013 ; 7819 160-172.[citado 2024 nov. 10 ] Available from: https://doi.org/10.1007/978-3-642-37456-2

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