This enables us to easily apply K-means clustering (from scikit-learn) to group up similar poses and separate dissimilar poses with the first two components which best represent the pair-wise similarity distances. A notable downside to K-means clustering is that one has to specify the number of clusters to be used to separate the data points. To avoid bias, we use silhouette scoring 42 (from scikit-learn) to automatically determine the best number of clusters to use to separate our data points. Silhouette scores range from -1 to +1 and evaluates how similar objects belonging to the same cluster are (cohesion) and how dissimilar those objects are with other clusters (separation) (Left: Fig. ). High values (closer to +1) indicate that the object is similar to other objects within the same cluster and dissimilar to objects in neighboring clusters. Low values (closer to -1) indicate that the object is dissimilar to others within the same clusters and similar to objects in neighboring clusters. A low total silhouette score indicates that the clustering configuration is likely incorrect with either too many or too few clusters, so we can use the total silhouette score as a way to select the optimal number of clusters.