Clustering methods become increasingly important in analyzing heterogeneity of treatment effects, especially in longitudinal behavioral intervention studies. Methods such as K-means and Fuzzy C-means (FCM) have been widely endorsed to identify distinct groups of different types of data. Build upon our MIFuzzy [1], our goal is to concurrently handle multiple methodological issues in studying high dimensional longitudinal intervention data with missing values. Particularly, this paper focuses on the initialization issue of FCM and proposes a new initialization method to overcome the local optimal problem and decrease the convergence time in handling high-dimensional data with missing values for overlapping clusters. Based on the idea of K-means++ [9], we proposed an enhanced Fuzzy C-means clustering (eFCM) and incorporated it into our MIFuzzy. This method was evaluated using real longitudinal intervention data, classic and generic datasets. Compared to conventional FCM, our findings indicate eFCM can improve computational efficiency and avoid the local optimization.