  In this report, we present an unsupervised machine learning method for
determining groups of molecular systems according to similarity in their
dynamics or structures using Ward's minimum variance objective function. We
first apply the minimum variance clustering to a set of simulated tripeptides
using the information theoretic Jensen-Shannon divergence between Markovian
transition matrices in order to gain insight into how point mutations affect
protein dynamics. Then, we extend the method to partition two chemoinformatic
datasets according to structural similarity to motivate a train/validation/test
split for supervised learning that avoids overfitting.
