Font Size: 

Class imbalance is a common problem in functional clustering where some clusters have significantly more curves than other clusters. In such cases, most clustering algorithms tend to prioritize the majority class, resulting in sub-optimal cluster assignments. We propose a functional iterative hierarchical clustering approach to address the issue of class imbalance in functional data clustering. The performance of the proposed approach is compared with existing approaches. The proposed approach yields more accurate cluster assignments and a more precise approximation of the average trajectory of the curves within each cluster.