Abstract
AbstractDensity-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 (coreset) Markov-state models. Here we present the opensource Python project CommonNNClustering, which provides an easy-to-use and efficient re-implementation of the commonnearest-neighbour (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 customisation.
Publisher
Cold Spring Harbor Laboratory
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