The Hierarchical Structure of Galactic Haloes: Generalised N-Dimensional
  Clustering with CluSTAR-ND
  We present CluSTAR-ND, a fast hierarchical galaxy/(sub)halo finder that
produces Clustering Structure via Transformative Aggregation and Rejection in
N-Dimensions. It is designed to improve upon Halo-OPTICS -- an algorithm that
automatically detects and extracts significant astrophysical clusters from the
3D spatial positions of simulation particles -- by decreasing run-times,
possessing the capability for metric adaptivity, and being readily applicable
to data with any number of features. We directly compare these algorithms and
find that not only does CluSTAR-ND produce a similarly robust clustering
structure, it does so in a run-time that is at least $3$ orders of magnitude
faster. In optimising CluSTAR-ND's clustering performance, we have also
effectively made CluSTAR-ND a parameterless clustering algorithm since its
parameters will be chosen automatically and optimally based on the input data.
We conclude that CluSTAR-ND is a robust astrophysical clustering algorithm that
can be leveraged to find stellar satellite groups on large synthetic or
observational data sets.
