Comparative Study of Clustering Algorithms in the Context of Education Data Mining - Bahrami, Ahmad Zia; Shahidzay, Amir Kror (Journal of Natural Sciences – Kabul University, 2023)
Educational data mining (EDM) transforms raw data from education systems into actionable insights that can significantly impact research and academic performance. This paper explores how universities can leverage academic data for strategic purposes by conducting a comparative study of clustering algorithms applied in academic data mining. It compares partition-based (K-Means), density-based (DBSCAN), and hierarchical (BIRCH) methods to determine the most suitable technique for clustering analysis in educational settings. The results indicate that the classification-based K-Means algorithm outperforms the hierarchical BIRCH algorithm and the density-based DBSCAN algorithm.