Application of data mining for the classification of university programs of industrial engineering accredited in high quality in Colombia

The present research article proposes a method to classify University engineering programs, placing special attention to relations between the subjects of the curriculum and the 12 areas of knowledge established in the body of competencies published by the Institute of industrial and System Engineers (IIES). Techniques of unsupervised data analysis such as Principal Components Analysis (PCA) and cluster analysis were used for the proposed classification. Twenty-one programs, accredited by high quality in Industrial Engineering in Colombia, are used as units of study. The results show that factors such as international accreditation, size of the faculties of engineering and University profile, influence the grouping of the programs of study. The research allowed to classify three large main components and profiles of accredited programs. © 2018 Centro de Informacion Tecnologica. All Rights Reserved.

• Fecha de publicación: 2018
• Institución: Universidad Tecnológica de Bolívar
• Repositorio: Repositorio Institucional UTB
• Idioma: eng
• OAI Identifier: oai:repositorio.utb.edu.co:20.500.12585/8729
• Acceso en línea: https://hdl.handle.net/20.500.12585/8729
• Palabra clave: Clustering; Data mining; Education; Industrial engineering; Principal components; Application programs; Cluster analysis; Data mining; Education; Industrial engineering; Industrial research; Clustering; Engineering program; International accreditation; Principal components; Principal components analysis; System engineers; University programs; Unsupervised data; Principal component analysis
• Rights: openAccess
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/