<![CDATA[Group Investigation (GI) is a widely used cooperative learning strategy in higher education, but challenges such as large class sizes and diverse student profiles complicate manual group formation. Previous studies have applied clustering algorithms like K-Means, yet K-Medoids, which is robust to noise, remain underexplored for group formation, especially GI. This study proposed a data-driven approach using the K-Medoids clustering algorithm to create student groups that are both interest-aligned and heterogeneous in profile, which enhancing the effectiveness of GI activities. Employing the Knowledge Discovery in Databases (KDD) framework, the process included data selection, preprocessing, transformation, three grouping processes, and evaluation were performed. In grouping process students were initially grouped by interest, clustered using K-Medoids with various distance measures tested, and finally, groups were adjusted to balance homogeneity and diversity. In grouping stage 2, clustering with Euclidean distance and PCA achieved the highest Silhouette Score, indicating superior grouping quality. The result of heterogeneity group of students evaluated with Gower dissimilarity shows that the method produces internally diverse yet cohesive interest groups, supporting GI goals.]]>
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