  New Physics can manifest itself in kinematic distributions of particle
decays. The parameter space defining the shape of such distributions can be
large which is challenging for both theoretical and experimental studies. Using
clustering algorithms, the parameter space can however be dissected into
subsets (clusters) which correspond to similar kinematic distributions.
Clusters can then be represented by benchmark points, which allow for less
involved studies and a concise presentation of the results. We demonstrate this
concept using the Python package ClusterKinG, an easy to use framework for the
clustering of distributions that particularly aims to make these techniques
more accessible in a High Energy Physics context. As an example we consider
$\bar B\to D^{(*)}\tau^-\bar\nu_\tau$ distributions and discuss various
clustering methods and possible implications for future experimental analyses.
