Analyzing and clustering students admission data in Yala Rajabhat University Thailand

Abstract

This research explores the use of clustering techniques to analyze student admission data at Yala Rajabhat University, Thailand, aiming to enhance recruitment strategies and understand student profiles. Employing K-means, Hierarchical Clustering, and Density-based spatial clustering of applications with noise (DBSCAN), the study groups admission data based on factors like educational institution, geographic location, and program chosen. The methodology incorporates normalization and principal component analysis (PCA) to ensure data quality, while the Elbow Method determines the optimal number of clusters for effective data segmentation. The davies-bouldin index (DBI) evaluates the clustering configurations, ensuring that clusters are well-separated and cohesive. The results reveal distinct student profiles that can inform targeted marketing and improve recruitment strategies. This study not only provides strategic insights into student recruitment but also contributes to the literature on the use of data science in educational settings, highlighting the transformative impact of advanced analytics on institutional effectiveness. The research emphasizes the importance of data-driven approaches in adapting to the changing dynamics of student admissions and the competitive landscape of higher education.