Segmentation of admitted student to a public university applying K-prototype algorithm


  • Ledvir Ayrton Walter Chávez Valderrama Universidad Nacional Agraria La Molina, Lima, Perú.
  • Jesús Walter Salinas Flores Universidad Nacional Agraria La Molina, Lima, Perú.



semantic maps, graphic organizers, written texts, reading comprehension, comprehension levels


Currently, data analysis is a challenging task, especially in the field of education, because in-depth research is carried out to know, understand and manage the diversity of students who enter for higher education institution and with it, to propose educational strategies to improve the teaching-learning model. The objective of this article was to characterize the pro|44file of admitted student of a public university with respect to their socio-demographic, economic and academic performance variables using K-prototypes algorithm. For this purpose, data from admitted, the entrant's file and their school certificate. It was possible to determine that admitted student fits with 5 profiles, each one with its own characteristics, allowing the grouping of students with similar characteristics, contributing to the improvement of support policies, promoting changes in favor of educational quality and promoting the renovation of teaching spaces in a personalized way around the student profile that the university manages.


Download data is not yet available.


Arias, J. (2015). El perfil de ingreso en el rendimiento académico inicial de los estudiantes de la carrera de Agronomía de la Universidad Nacional Agraria La Molina, años 2011 a 2012. [Tesis de doctorado]. Universidad Nacional de Educación Enrique Guzmán y Valle.

Chun, L. (2012). Diseño e Implementación de algoritmos aproximados de clustering balanceado en PSO. [Tesis de maestría]. Universidad de Chile.

Hartigan, J y Wong, M. (1979). Algorithm AS 136: A K-Means clustering algorithm. Journal of the Royal Statistical Society, 28(1), 100-108. .

Huang, Z. (1998). Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values. Data Mining and Knowledge Discovery, 2, 283 - 304. .

Kaur, A; Kaur, N. (2013). Survey Paper on Clustering Techniques. International Journal of Science, Engineering and Technology Research (IJSETR), 2(4), 803-806. .

MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Berkeley Symposium on Mathematical Statistics and Probability, 281-297. .

Rai, P y Singh, S. (2010). A Survey of Clustering Techniques. International Journal of Computer Applications, 7(12), 1-5. .

Wang, W y Zhang, Y. (2007). On fuzzy cluster validity indices. Fuzzy Sets and Systems. Fuzzy Sets and Systems, 158(19), 2095-2117.



How to Cite

Chavez, L., & Salinas, J. (2021). Segmentation of admitted student to a public university applying K-prototype algorithm . ierra uestra, 15(2), 10–21.