CommonNNClustering?A Python Package for Generic Common-Nearest-Neighbor Clustering

Density-based clustering procedures are widely used in a variety of data science applications. Their advantage lies in the capability to find arbitrarily shaped and sized clusters and robustness against outliers. In particular, they proved effective in the analysis of molecular dynamics simulations, where they serve to identify relevant, low-energetic molecular conformations. As such, they can provide a convenient basis for the construction of kinetic (core-set) Markov-state models. Here we present the open-source Python project CommonNNClustering, which provides an easy-to-use and efficient reimplementation of the common-nearestneighbor (CommonNN) method. The package provides functionalities for hierarchical clustering and an evaluation of the results. We put our emphasis on a generic API design to keep the implementation flexible and open for customization.

Automated summary

Density-based clustering procedures are widely used due to their ability to find arbitrary shaped clusters and their robustness against outliers. They have been proven effective in molecular dynamics simulations for identifying relevant molecular conformations. The open-source Python project CommonNNClustering provides a convenient reimplementation of the common-nearestneighbor method, offering hierarchical clustering and result evaluation functionalities with a focus on flexibility and customization through a generic API design.