Integration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled Architecture

Autores:: Vallejo-Cabrera, Fernando-Mauricio
Timaran-Pereira, Ricardo
Chaves-Torres, Anivar

Tipo de recurso:: Article of journal

Fecha de publicación:: 2026

Institución:: Universidad Pedagógica y Tecnológica de Colombia

Repositorio:: RiUPTC: Repositorio Institucional UPTC

Idioma:

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dc.title.en-US.fl_str_mv	Integration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled Architecture
title	Integration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled Architecture
spellingShingle	Integration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled Architecture Clustering algorithms Data analysis Data mining Databases Machine learning Python Unsupervised learning Algoritmos de clustering Análisis de datos Aprendizaje automático Aprendizaje no supervisado Bases de datos Minería de datos
title_short	Integration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled Architecture
title_full	Integration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled Architecture
title_fullStr	Integration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled Architecture
title_full_unstemmed	Integration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled Architecture
title_sort	Integration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled Architecture
dc.creator.fl_str_mv	Vallejo-Cabrera, Fernando-Mauricio Timaran-Pereira, Ricardo Chaves-Torres, Anivar
dc.contributor.author.eng.fl_str_mv	Vallejo-Cabrera, Fernando-Mauricio Timaran-Pereira, Ricardo Chaves-Torres, Anivar
dc.subject.none.fl_str_mv	Clustering algorithms Data analysis Data mining Databases Machine learning Python Unsupervised learning Algoritmos de clustering Análisis de datos Aprendizaje automático Aprendizaje no supervisado Bases de datos Minería de datos
topic	Clustering algorithms Data analysis Data mining Databases Machine learning Python Unsupervised learning Algoritmos de clustering Análisis de datos Aprendizaje automático Aprendizaje no supervisado Bases de datos Minería de datos
publishDate	2026
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dc.date.available.none.fl_str_mv	2026-04-02 00:00:00
dc.date.issued.none.fl_str_mv	2026-04-02
dc.type.spa.fl_str_mv	Artículo de revista
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dc.relation.references.none.fl_str_mv	Y. R. Aranda, A. R. Sotolongo, “Integración de los algoritmos de minería de datos 1R, PRISM e ID3 a PostgreSQL,” Journal of Information Systems and Technology Management, vol. 10, no. 2, pp. 389-406, 2013. https://doi.org/10.4301/s1807-17752013000200012 A. Montero, J. M. Reyes, J. C. Díaz, “Integrando minería de reglas de asociación en PostgreSQL,” Ingeniería Solidaria, vol. 15, no. 11, pp. 24-36, 2022. M. A. García Vico, P. González García, C. J. Carmona, “Modelos descriptivos basados en aprendizaje supervisado para el tratamiento de grandes volúmenes de datos y flujos continuos de datos,” en Actas de la XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), Granada, España, 2018, pp. 1402–1407. https://sci2s.ugr.es/caepia18/proceedings/docs/CAEPIA2018_paper_293.pdf V. Valcárcel Asencios, “Data Mining y el descubrimiento del conocimiento,” Industrial Data, vol. 7, no. 2, pp. 83-86, 2004. https://doi.org/10.15381/idata.v7i2.6140 R. Saravanan, P. Sujatha, “A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification,” in Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp. 945-949, 2018. https://doi.org/10.1109/ICCONS.2018.8663155 B. Mahesh, “Machine Learning Algorithms - A Review,” International Journal of Science and Research, vol. 9, no. 1, pp. 381-386, Jan. 2020. https://doi.org/10.21275/ART20203995 N. M. Mahfuz, M. Yusoff, Z. Idrus, “Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure,” International Journal of Electrical and Computer Engineering, vol. 13, no. 1, pp. 1048-1059, Feb. 2023. https://doi.org/10.11591/ijece.v13i1.pp1048-1059 S. Singh, S. Srivastava, “Review of Clustering Techniques in Control System,” Procedia Computer Science, vol. 173, pp. 272-280, 2020. https://doi.org/10.1016/j.procs.2020.06.032 J. Alzubi, A. Nayyar, A. Kumar, “Machine Learning from Theory to Algorithms: An Overview,” Journal of Physics: Conference Series, vol. 1142, no. 1, e012012, 2018. https://doi.org/10.1088/1742-6596/1142/1/012012 Y. Rivera, Desarrollo de árboles de decisión como extensión al gestor de bases de datos PostgreSQL, Grade Thesis, Universidad de Ciencias Informática, La Habana, Cuba, 2014. https://repositorio.uci.cu/jspui/handle/ident/9264 R. Timarán, “Arquitecturas de Integración del Proceso de Descubrimiento de Conocimiento con Sistemas de Gestión de Bases de Datos: Un Estado del Arte,” Ingeniería y Competitividad, vol. 3, no. 2, pp. 45-55, 2011. https://doi.org/10.25100/iyc.v3i2.2327 R. Timarán, A. Chavez, “Integration of Supervised Machine Learning Techniques with Postgresql Dbms in a Middly Coupled Architecture,” en Encuentro Internacional de Educación en Ingenieria (EIEI ACOFI), 2023. https://doi.org/10.26507/paper.2982 Á. F. Calderón-Romero, A. O. Y Ramírez-Freyre, I. D. Alvarado, J. C. Guevara-Unigarro, Tariykdd: una herramienta genérica de descubrimiento de conocimiento en bases de datos débilmente acoplada con el SGBD PostgreSQL, Grade Thesis, Universida de Nariño, Pasto, Colombia, 2007. http://sired.udenar.edu.co/id/eprint/5744 R. Timarán, M. Millán, “New algebraic operators and SQL primitives for mining classification rules,” International Journal of Robotics Research, vol. 30, no. 2, pp. 115–123, May 2015. C. Schröer, F. Kruse, J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” Procedia in Computer Science, vol. 181, pp. 526-534, 2021. https://doi.org/10.1016/j.procs.2021.01.199
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dc.relation.ispartofjournal.eng.fl_str_mv	Revista Facultad de Ingeniería
dc.relation.bitstream.none.fl_str_mv	https://revistas.uptc.edu.co/index.php/ingenieria/article/download/20737/16947
dc.relation.citationedition.eng.fl_str_mv	Núm. 74 , Año 2025 : October-December 2025 (Continuos Publication)
dc.rights.none.fl_str_mv	Fernando-Mauricio Vallejo-Cabrera, Ricardo Timaran-Pereira, Anivar Chaves-Torres - 2025
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spelling	Vallejo-Cabrera, Fernando-MauricioTimaran-Pereira, RicardoChaves-Torres, Anivar2026-04-02 00:00:002026-04-16T17:47:53Z2026-04-02 00:00:002026-04-020121-1129https://repositorio.uptc.edu.co/handle/001/2162010.19053/uptc.01211129.v34.n74.2025.207372357-5328https://doi.org/10.19053/uptc.01211129.v34.n74.2025.20737application/pdfUniversidad Pedagógica y Tecnológica de ColombiaFernando-Mauricio Vallejo-Cabrera, Ricardo Timaran-Pereira, Anivar Chaves-Torres - 2025https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.uptc.edu.co/index.php/ingenieria/article/view/20737Clustering algorithmsData analysisData miningDatabasesMachine learningPythonUnsupervised learningAlgoritmos de clusteringAnálisis de datosAprendizaje automáticoAprendizaje no supervisadoBases de datosMinería de datosIntegration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled ArchitectureArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articleJournal articleinfo:eu-repo/semantics/publishedVersionTexthttp://purl.org/coar/version/c_970fb48d4fbd8a85Y. R. Aranda, A. R. Sotolongo, “Integración de los algoritmos de minería de datos 1R, PRISM e ID3 a PostgreSQL,” Journal of Information Systems and Technology Management, vol. 10, no. 2, pp. 389-406, 2013. https://doi.org/10.4301/s1807-17752013000200012A. Montero, J. M. Reyes, J. C. Díaz, “Integrando minería de reglas de asociación en PostgreSQL,” Ingeniería Solidaria, vol. 15, no. 11, pp. 24-36, 2022.M. A. García Vico, P. González García, C. J. Carmona, “Modelos descriptivos basados en aprendizaje supervisado para el tratamiento de grandes volúmenes de datos y flujos continuos de datos,” en Actas de la XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA), Granada, España, 2018, pp. 1402–1407. https://sci2s.ugr.es/caepia18/proceedings/docs/CAEPIA2018_paper_293.pdfV. Valcárcel Asencios, “Data Mining y el descubrimiento del conocimiento,” Industrial Data, vol. 7, no. 2, pp. 83-86, 2004. https://doi.org/10.15381/idata.v7i2.6140R. Saravanan, P. Sujatha, “A State of Art Techniques on Machine Learning Algorithms: A Perspective of Supervised Learning Approaches in Data Classification,” in Second International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp. 945-949, 2018. https://doi.org/10.1109/ICCONS.2018.8663155B. Mahesh, “Machine Learning Algorithms - A Review,” International Journal of Science and Research, vol. 9, no. 1, pp. 381-386, Jan. 2020. https://doi.org/10.21275/ART20203995N. M. Mahfuz, M. Yusoff, Z. Idrus, “Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure,” International Journal of Electrical and Computer Engineering, vol. 13, no. 1, pp. 1048-1059, Feb. 2023. https://doi.org/10.11591/ijece.v13i1.pp1048-1059S. Singh, S. Srivastava, “Review of Clustering Techniques in Control System,” Procedia Computer Science, vol. 173, pp. 272-280, 2020. https://doi.org/10.1016/j.procs.2020.06.032J. Alzubi, A. Nayyar, A. Kumar, “Machine Learning from Theory to Algorithms: An Overview,” Journal of Physics: Conference Series, vol. 1142, no. 1, e012012, 2018. https://doi.org/10.1088/1742-6596/1142/1/012012Y. Rivera, Desarrollo de árboles de decisión como extensión al gestor de bases de datos PostgreSQL, Grade Thesis, Universidad de Ciencias Informática, La Habana, Cuba, 2014. https://repositorio.uci.cu/jspui/handle/ident/9264R. Timarán, “Arquitecturas de Integración del Proceso de Descubrimiento de Conocimiento con Sistemas de Gestión de Bases de Datos: Un Estado del Arte,” Ingeniería y Competitividad, vol. 3, no. 2, pp. 45-55, 2011. https://doi.org/10.25100/iyc.v3i2.2327R. Timarán, A. Chavez, “Integration of Supervised Machine Learning Techniques with Postgresql Dbms in a Middly Coupled Architecture,” en Encuentro Internacional de Educación en Ingenieria (EIEI ACOFI), 2023. https://doi.org/10.26507/paper.2982Á. F. Calderón-Romero, A. O. Y Ramírez-Freyre, I. D. Alvarado, J. C. Guevara-Unigarro, Tariykdd: una herramienta genérica de descubrimiento de conocimiento en bases de datos débilmente acoplada con el SGBD PostgreSQL, Grade Thesis, Universida de Nariño, Pasto, Colombia, 2007. http://sired.udenar.edu.co/id/eprint/5744R. Timarán, M. Millán, “New algebraic operators and SQL primitives for mining classification rules,” International Journal of Robotics Research, vol. 30, no. 2, pp. 115–123, May 2015.C. Schröer, F. Kruse, J. M. Gómez, “A systematic literature review on applying CRISP-DM process model,” Procedia in Computer Science, vol. 181, pp. 526-534, 2021. https://doi.org/10.1016/j.procs.2021.01.199e2073774e2073734Revista Facultad de Ingenieríahttps://revistas.uptc.edu.co/index.php/ingenieria/article/download/20737/16947Núm. 74 , Año 2025 : October-December 2025 (Continuos Publication)PublicationOREORE.xmltext/xml2762https://repositorio.uptc.edu.co/bitstreams/a02b7c51-06b9-4184-805e-52b06594804b/download7781db6b2bc23233db4790dbb26a571fMD51falseAnonymousREAD001/21620oai:repositorio.uptc.edu.co:001/216202026-10-29 14:05:26.837https://creativecommons.org/licenses/by/4.0metadata.onlyhttps://repositorio.uptc.edu.coRepositorio Institucional UPTCbdigital@metabiblioteca.com

Integration of the K-means Algorithm into PostgreSQL through PL/Python Extensions: A Moderately Coupled Architecture

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