  We build upon Ordering Points To Identify Clustering Structure (OPTICS), a
hierarchical clustering algorithm well-known to be a robust data-miner, in
order to produce Halo-OPTICS, an algorithm designed for the automatic detection
and extraction of all meaningful clusters between any two arbitrary sizes. We
then apply Halo-OPTICS to the 3D spatial positions of halo particles within
four separate synthetic Milky Way type galaxies, classifying the stellar and
dark matter structural hierarchies. Through visualisation of the Halo-OPTICS
output, we compare its structure identification to the state-of-the-art
galaxy/(sub)halo finder VELOCIraptor, finding excellent agreement even though
Halo-OPTICS does not consider kinematic information in this current
implementation. We conclude that Halo-OPTICS is a robust hierarchical halo
finder, although its determination of lower spatial-density features such as
the tails of streams could be improved with the inclusion of extra localised
information such as particle kinematics and stellar metallicity into its
distance metric.

Astrophysics of Galaxies