Data mining techniques and multivariate analysis to discover Patterns in university final researches

The aim of this study is to extract knowledge from the final researches of the Mumbai University Science Faculty. Five classification models were applied: Vector Support Machines, Neural Networks, Decision Tree, Random Forest and Powering; considering the Experiment Design and Multivariate Analysis Lines. Results showed that for the Experiment Design line, the most accurate model was Random Forest with 71.48% predictions that are correct respecting to the total. Regarding the Multivariate Analysis line, there was no significant difference in overall accuracy, fluctuating by 97%.

• Autores: amelec, viloria; Rodríguez López, Jorge; García Leyva, Diana Margarita; Vargas Mercado, Carlos; Hernández-Palma, Hugo; ORELLANO LLINAS, NATALY; Arrozola David, Mónica; Velasquez Rodriguez, Javier
• Tipo de recurso: Article of journal
• Fecha de publicación: 2019
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/5867
• Acceso en línea: https://hdl.handle.net/11323/5867; https://repositorio.cuc.edu.co/
• Palabra clave: Data mining education; Education indicators; Classification.; Data mining techniques; Educación en minería de datos; Técnicas de minería de datos; Indicadores de educación; Clasificación
• Rights: openAccess
• License: http://creativecommons.org/publicdomain/zero/1.0/