Fig. 2.

Comparison of Clustering Algorithms on a 2D Principal Component Analysis (PCA) Projection. Each panel represents the clusters formed by a different algorithm on the same dataset projected onto the first two principal components. The colors indicate the cluster assignments by each algorithm. (A) KMeans clustering showing three distinct groups. (B) Agglomerative clustering with a similar three-group distinction. (C) Spectral clustering which has identified two groups. (D) Gaussian Mixture Model (GMM) with a distribution that also suggests three groups. The study shows that utilizing various clustering methods such as KMeans, GMM, and Agglomerative effectively separates CVID samples into three distinct clusters.