Comparison of Unsupervised Learning Algorithms for Clustering Cuban Citizens using a Lifestyle Questionnaire

S. Torres; D. Alonso; N. Martinez; S. Merced

doi:10.31926/but.shk.2024.17.66.1.9

Authors

S. Torres University of Informatics Sciences, Cuba
D. Alonso University of Informatics Sciences, Cuba
N. Martinez University of Informatics Sciences, Cuba
S. Merced University of the Sciences of the Physical Culture and the Sport "Manuel Fajardo", Cuba

DOI:

https://doi.org/10.31926/but.shk.2024.17.66.1.9

Keywords:

algorithms, clustering, density, habits, life

Abstract

This study uses information technologies to analyze the lifestyles of Cubans. Cluster analysis is used to identify similarities in habits and lifestyles. Clustering results are compared using K-Means, DBSCAN, and HDBSCAN algorithms. Principal Component Analysis is applied to visualize the dataset. Internal validation metrics are defined to evaluate the performance of the algorithms. The results indicate that K-Means provides better clustering for this dataset.