  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.
