In eq 11, d scale is an arbitrary scaling factor. To improve efficiency when dealing with large datasets containing primarily unfolded, "noisy" structures, we have implemented a preclustering filtering function, to remove data points from the distance matrix which have few near neighbors. The fraction of data passing through the density filter is specified by the user, and a neighborhood scan radius parameter and minimum number of neighbors within that radius are optimized to achieve the specified filtering criteria. As a result, only relatively high-density data points are input into the DBSCAN algorithm, allowing the use of a smaller epsilon parameter to identify distinct structures more precisely. A similar density filtering pre-clustering step has been successfully used to identify protein-ligand binding sites in a noisy trajectory dataset.