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Filtros : "ICMC" "Campello, Ricardo José Gabrielli Barreto" Limpar

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  • 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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      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: 23 abr. 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 abr. 23 ] 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 abr. 23 ] Available from: https://doi.org/10.5441/002/edbt.2014.31

  • Trabalho de evento-anais periodico

    On the selection of appropriate distances for gene expression data clustering (2014)

    Jaskowiak, Pablo A; Campello, Ricardo José Gabrielli Barreto; Costa, Ivan G

    Source: BMC Bioinformatics. Conference titles: Asia Pacific Bioinformatics Conference - APBC. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      JASKOWIAK, Pablo A e CAMPELLO, Ricardo José Gabrielli Barreto e COSTA, Ivan G. On the selection of appropriate distances for gene expression data clustering. BMC Bioinformatics. London: BioMed Central. Disponível em: https://doi.org/10.1186/1471-2105-15-S2-S2. Acesso em: 23 abr. 2024. , 2014

    • APA

      Jaskowiak, P. A., Campello, R. J. G. B., & Costa, I. G. (2014). On the selection of appropriate distances for gene expression data clustering. BMC Bioinformatics. London: BioMed Central. doi:10.1186/1471-2105-15-S2-S2

    • NLM

      Jaskowiak PA, Campello RJGB, Costa IG. On the selection of appropriate distances for gene expression data clustering [Internet]. BMC Bioinformatics. 2014 ; 15 1-17.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1186/1471-2105-15-S2-S2

    • Vancouver

      Jaskowiak PA, Campello RJGB, Costa IG. On the selection of appropriate distances for gene expression data clustering [Internet]. BMC Bioinformatics. 2014 ; 15 1-17.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1186/1471-2105-15-S2-S2

  • 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

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      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: 23 abr. 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 abr. 23 ] 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 abr. 23 ] 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

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      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: 23 abr. 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 abr. 23 ] 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 abr. 23 ] Available from: https://doi.org/10.1007/978-1-4614-6170-8_356

  • Artigo de periodico

    Similarity measures for comparing biclusterings (2014)

    Horta, Danilo; Campello, Ricardo José Gabrielli Barreto

    Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      HORTA, Danilo e CAMPELLO, Ricardo José Gabrielli Barreto. Similarity measures for comparing biclusterings. IEEE/ACM Transactions on Computational Biology and Bioinformatics, v. 11, n. 5, p. 942-954, 2014Tradução . . Disponível em: https://doi.org/10.1109/TCBB.2014.2325016. Acesso em: 23 abr. 2024.

    • APA

      Horta, D., & Campello, R. J. G. B. (2014). Similarity measures for comparing biclusterings. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11( 5), 942-954. doi:10.1109/TCBB.2014.2325016

    • NLM

      Horta D, Campello RJGB. Similarity measures for comparing biclusterings [Internet]. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2014 ; 11( 5): 942-954.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1109/TCBB.2014.2325016

    • Vancouver

      Horta D, Campello RJGB. Similarity measures for comparing biclusterings [Internet]. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2014 ; 11( 5): 942-954.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1109/TCBB.2014.2325016

  • 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

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    • ABNT

      MOULAVI, Davoud et al. Density-based clustering validation. 2014, Anais.. Philadelphia: SIAM, 2014. . Acesso em: 23 abr. 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 abr. 23 ]

    • Vancouver

      Moulavi D, Jaskowiak PA, Campello RJGB, Zimek A, Sander J. Density-based clustering validation. Proceedings. 2014 ;[citado 2024 abr. 23 ]

  • Trabalho de evento-anais periodico

    Evolutionary k-means for distributed data sets (2014)

    Naldi, M. C; Campello, Ricardo José Gabrielli Barreto

    Source: Neurocomputing. Conference titles: Brazilian Symposium on Neural Networks - SBRN. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      NALDI, M. C e CAMPELLO, Ricardo José Gabrielli Barreto. Evolutionary k-means for distributed data sets. Neurocomputing. Amsterdam: Elsevier. Disponível em: https://doi.org/10.1016/j.neucom.2013.05.046. Acesso em: 23 abr. 2024. , 2014

    • APA

      Naldi, M. C., & Campello, R. J. G. B. (2014). Evolutionary k-means for distributed data sets. Neurocomputing. Amsterdam: Elsevier. doi:10.1016/j.neucom.2013.05.046

    • NLM

      Naldi MC, Campello RJGB. Evolutionary k-means for distributed data sets [Internet]. Neurocomputing. 2014 ; 127 30-42.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1016/j.neucom.2013.05.046

    • Vancouver

      Naldi MC, Campello RJGB. Evolutionary k-means for distributed data sets [Internet]. Neurocomputing. 2014 ; 127 30-42.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1016/j.neucom.2013.05.046

  • 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

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      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: 23 abr. 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 abr. 23 ] 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 abr. 23 ] Available from: https://doi.org/10.1007/978-3-319-06483-3_16

  • Tese (Doutorado)

    Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento (2013)

    Horta, Danilo; Campello, Ricardo José Gabrielli Barreto (Orientador)

    Unidade: ICMC

    Subjects: APRENDIZADO COMPUTACIONAL, MINERAÇÃO DE DADOS

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      HORTA, Danilo. Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento. 2013. Tese (Doutorado) – Universidade de São Paulo, São Carlos, 2013. Disponível em: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14012014-154211/. Acesso em: 23 abr. 2024.

    • APA

      Horta, D. (2013). Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento (Tese (Doutorado). Universidade de São Paulo, São Carlos. Recuperado de http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14012014-154211/

    • NLM

      Horta D. Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento [Internet]. 2013 ;[citado 2024 abr. 23 ] Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14012014-154211/

    • Vancouver

      Horta D. Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento [Internet]. 2013 ;[citado 2024 abr. 23 ] Available from: http://www.teses.usp.br/teses/disponiveis/55/55134/tde-14012014-154211/

  • 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

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    • 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: 23 abr. 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 abr. 23 ] 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 abr. 23 ] 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

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      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: 23 abr. 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 abr. 23 ] 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 abr. 23 ] Available from: https://doi.org/10.1145/2487575.2487676

  • Trabalho de evento

    Distributed k-means clustering with low transmission cost (2013)

    Naldi, Murilo Coelho; Campello, Ricardo José Gabrielli Barreto

    Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      NALDI, Murilo Coelho e CAMPELLO, Ricardo José Gabrielli Barreto. Distributed k-means clustering with low transmission cost. 2013, Anais.. Los Alamitos: Conference Publishing Services, 2013. Disponível em: https://doi.org/10.1109/BRACIS.2013.20. Acesso em: 23 abr. 2024.

    • APA

      Naldi, M. C., & Campello, R. J. G. B. (2013). Distributed k-means clustering with low transmission cost. In Proceedings. Los Alamitos: Conference Publishing Services. doi:10.1109/BRACIS.2013.20

    • NLM

      Naldi MC, Campello RJGB. Distributed k-means clustering with low transmission cost [Internet]. Proceedings. 2013 ;[citado 2024 abr. 23 ] Available from: https://doi.org/10.1109/BRACIS.2013.20

    • Vancouver

      Naldi MC, Campello RJGB. Distributed k-means clustering with low transmission cost [Internet]. Proceedings. 2013 ;[citado 2024 abr. 23 ] Available from: https://doi.org/10.1109/BRACIS.2013.20

  • 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

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      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: 23 abr. 2024.

    • 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 2024 abr. 23 ] 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 2024 abr. 23 ] Available from: https://doi.org/10.1007/s10618-012-0290-x

  • Artigo de periodico

    Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis (2013)

    Jaskowiak, Pablo A; Campello, Ricardo José Gabrielli Barreto; Costa, Ivan G

    Source: IEEE/ACM Transactions on Computational Biology and Bioinformatics. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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      JASKOWIAK, Pablo A e CAMPELLO, Ricardo José Gabrielli Barreto e COSTA, Ivan G. Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, v. 10, n. 4, p. 845-857, 2013Tradução . . Disponível em: https://doi.org/10.1109/TCBB.2013.9. Acesso em: 23 abr. 2024.

    • APA

      Jaskowiak, P. A., Campello, R. J. G. B., & Costa, I. G. (2013). Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 10( 4), 845-857. doi:10.1109/TCBB.2013.9

    • NLM

      Jaskowiak PA, Campello RJGB, Costa IG. Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis [Internet]. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2013 ; 10( 4): 845-857.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1109/TCBB.2013.9

    • Vancouver

      Jaskowiak PA, Campello RJGB, Costa IG. Proximity measures for clustering gene expression microarray data: a validation methodology and a comparative analysis [Internet]. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2013 ; 10( 4): 845-857.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1109/TCBB.2013.9

  • 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

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      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: 23 abr. 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 abr. 23 ] 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 abr. 23 ] 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

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      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: 23 abr. 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 abr. 23 ] 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 abr. 23 ] 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

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    • 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: 23 abr. 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 abr. 23 ] 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 abr. 23 ] Available from: https://doi.org/10.1007/978-3-642-37456-2

  • Trabalho de evento

    On the combination of relative clustering validity criteria (2013)

    Vendramin, Lucas; Jaskowiak, Pablo A; Campello, Ricardo José Gabrielli Barreto

    Source: Proceedings. Conference titles: International Conference on Scientific and Statistical Database Management - SSDBM. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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    • ABNT

      VENDRAMIN, Lucas e JASKOWIAK, Pablo A e CAMPELLO, Ricardo José Gabrielli Barreto. On the combination of relative clustering validity criteria. 2013, Anais.. New York: ACM, 2013. Disponível em: https://doi.org/10.1145/2484838.2484844. Acesso em: 23 abr. 2024.

    • APA

      Vendramin, L., Jaskowiak, P. A., & Campello, R. J. G. B. (2013). On the combination of relative clustering validity criteria. In Proceedings. New York: ACM. doi:10.1145/2484838.2484844

    • NLM

      Vendramin L, Jaskowiak PA, Campello RJGB. On the combination of relative clustering validity criteria [Internet]. Proceedings. 2013 ;[citado 2024 abr. 23 ] Available from: https://doi.org/10.1145/2484838.2484844

    • Vancouver

      Vendramin L, Jaskowiak PA, Campello RJGB. On the combination of relative clustering validity criteria [Internet]. Proceedings. 2013 ;[citado 2024 abr. 23 ] Available from: https://doi.org/10.1145/2484838.2484844

  • Artigo de periodico

    Takagi-Sugeno fuzzy models in the framework of orthonormal basis functions (2013)

    Machado, Jeremias B; Campello, Ricardo José Gabrielli Barreto; Amaral, Wagner Caradori

    Source: IEEE Transactions on Cybernetics. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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    • ABNT

      MACHADO, Jeremias B e CAMPELLO, Ricardo José Gabrielli Barreto e AMARAL, Wagner Caradori. Takagi-Sugeno fuzzy models in the framework of orthonormal basis functions. IEEE Transactions on Cybernetics, v. 43, n. ju 2013, p. 858-870, 2013Tradução . . Disponível em: https://doi.org/10.1109/TSMCB.2012.2217323. Acesso em: 23 abr. 2024.

    • APA

      Machado, J. B., Campello, R. J. G. B., & Amaral, W. C. (2013). Takagi-Sugeno fuzzy models in the framework of orthonormal basis functions. IEEE Transactions on Cybernetics, 43( ju 2013), 858-870. doi:10.1109/TSMCB.2012.2217323

    • NLM

      Machado JB, Campello RJGB, Amaral WC. Takagi-Sugeno fuzzy models in the framework of orthonormal basis functions [Internet]. IEEE Transactions on Cybernetics. 2013 ; 43( ju 2013): 858-870.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1109/TSMCB.2012.2217323

    • Vancouver

      Machado JB, Campello RJGB, Amaral WC. Takagi-Sugeno fuzzy models in the framework of orthonormal basis functions [Internet]. IEEE Transactions on Cybernetics. 2013 ; 43( ju 2013): 858-870.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1109/TSMCB.2012.2217323

  • Artigo de periodico

    Collaborative fuzzy clustering algorithms: some refinements and design guidelines (2012)

    Colleta, Luiz F. S.; Vendramin, Lucas; Hruschka, Eduardo Raul ; Campello, Ricardo José Gabrielli Barreto; Pedrycz, Witold

    Source: IEEE Transactions on Fuzzy Systems. Unidade: ICMC

    Assunto: INTELIGÊNCIA ARTIFICIAL

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    • ABNT

      COLLETA, Luiz F. S. et al. Collaborative fuzzy clustering algorithms: some refinements and design guidelines. IEEE Transactions on Fuzzy Systems, v. 20, n. ju 2012, p. 444-462, 2012Tradução . . Disponível em: https://doi.org/10.1109/TFUZZ.2011.2175400. Acesso em: 23 abr. 2024.

    • APA

      Colleta, L. F. S., Vendramin, L., Hruschka, E. R., Campello, R. J. G. B., & Pedrycz, W. (2012). Collaborative fuzzy clustering algorithms: some refinements and design guidelines. IEEE Transactions on Fuzzy Systems, 20( ju 2012), 444-462. doi:10.1109/TFUZZ.2011.2175400

    • NLM

      Colleta LFS, Vendramin L, Hruschka ER, Campello RJGB, Pedrycz W. Collaborative fuzzy clustering algorithms: some refinements and design guidelines [Internet]. IEEE Transactions on Fuzzy Systems. 2012 ; 20( ju 2012): 444-462.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1109/TFUZZ.2011.2175400

    • Vancouver

      Colleta LFS, Vendramin L, Hruschka ER, Campello RJGB, Pedrycz W. Collaborative fuzzy clustering algorithms: some refinements and design guidelines [Internet]. IEEE Transactions on Fuzzy Systems. 2012 ; 20( ju 2012): 444-462.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1109/TFUZZ.2011.2175400

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