Modified FDP cluster algorithm and its application in protein conformation clustering analysis

Abstract We present a modified find density peaks (MFDP) clustering algorithm. In the MFDP, a critical parameter, dc, is auto-defined by minimizing the entropy of all points. By considering both the point density, ρ, and large distance from points with higher densities, δ, the high-dimensional points are transformed into a 2D space. The halo points of the original FDP cluster algorithm are redefined, and a definition of boundary points is introduced to illustrate the intersection region between clusters. To demonstrate the clustering ability, the distance-based K-means clustering and density-based algorithms DBSCAN, original FDP are employed respectively. Four criteria are introduced to evaluate the clustering algorithms quantitatively. For most of the cases, the MFDP provides a superior clustering result than both of the typical clustering algorithms, and FDP in 20 commonly used benchmark datasets, particularly in clearly depicting the intersection region between clusters. Finally, we evaluate the performance of the MFDP in the cluster analysis of conformations in molecular dynamics (MD). In the MD clustering process, eight typical cluster center conformations are selected in six collective variable spaces. Moreover, it is in strong agreement with the experiment results. The clustering results demonstrate the potential for generalized applications of the modified algorithm to similar problems.