Once appropriate features are selected, there are a myriad of approaches to cluster them into discrete states. One major method is a hybrid k-centers/k-medoids approach . In brief, for every pair of features a distance metric, such as the root mean square deviation (RMSD) is computed. The k-centers algorithm then (i) chooses an initial cluster center either as a predetermined point or randomly, and then all points are assigned to this initial cluster. (ii) The distance between every point and its assigned cluster center is then computed. (iii) The point with the largest distance to the assigned cluster center is then labelled as a new cluster center. (iv) The distances between all points and all cluster centers are recalculated, and points are reassigned to their closest cluster center based on these new labels. Steps (iiiv) are repeated until the maximum distance from any point to its assigned cluster center goes below a specified threshold, or a maximum number of cluster centers is reached.