This study explores the identification of student profiles with parental responsibilities at the National University of Altiplano, Puno, using clustering algorithms. A total of 206 records of students with parental responsibilities were analyzed, employing a variety of sociodemographic, academic, and family-related variables. Dimensionality reduction was performed using Principal Component Analysis (PCA), retaining 10 key components to enhance computational efficiency and interpretability. Following this, three clustering algorithms—K-Means, DBSCAN, and Agglomerative Clustering—were implemented to segment students and evaluate the effectiveness of these methods in identifying distinct behavioral profiles based on their responsibilities and academic challenges.

The K-Means algorithm proved to be the most effective, generating two distinct clusters with a Silhouette Score of 0.1611, a Davies-Bouldin Index of 2.1475, and a Calinski-Harabasz Index of 33.7629. Cluster 0 included students with greater academic stability and fewer interruptions, while Cluster 1 represented students facing greater challenges, such as frequent study pauses and lower academic performance. DBSCAN identified a noise cluster, while Agglomerative Clustering produced intermediate results with less defined clusters.

These findings underscore the usefulness of clustering techniques in understanding the academic dynamics of students with parental responsibilities, offering valuable insights for developing personalized interventions. This approach helps fill a gap in the existing literature and provides opportunities for future research with larger datasets and additional variables, ultimately improving support strategies for this unique student population