Comparison of Unsupervised Learning Algorithms for Clustering Cuban Citizens using a Lifestyle Questionnaire - S. Torres; D. Alonso; N. Martinez; S. Merced (Bulletin of the Transilvania University of Braşov: Series IX Sciences of Human Kinetics)
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.