Figure 4.

Results of STP-based clustering analysis for the entire tested population. (A) Principle Component Analysis (PCA): all the spatio-temporal features available from SONDA (see Supplementary Material) were used as an input for the PCA. The resulting components were processed with t-SNE to represent the high-dimensionality dataset into a lower-dimensionality space for clustering purposes. (B) Computation of the optimal number of clusters using the “elbow method” with the Within-clusters Sum of Squares (WSS) as a parameter. (C) Result of the k-means clustering algorithm (k = 5) applied to the outcome components of t-SNE.