Selecting representative samples from complex biological datasets using k-medoids clustering

This method quantifies the relationships/similarities among samples using their Euclidian distances by vectorizing all given properties, and then determines an appropriate sample size by evaluating the coverage of key proprieties from multiple candidate sizes, following by a k-medoids clustering to group samples into several clusters, and selects centers from each cluster as the most representatives.