  We test the ability of two unsupervised machine learning algorithms,
\textit{EnLink} and Shared Nearest Neighbour (SNN), to identify stellar
groupings in the Orion star-forming complex as an application to the
5-dimensional astrometric data from \textit{Gaia} DR2. The algorithms represent
two distinct approaches to limiting user bias when selecting parameter values
and evaluating the relative weights among astrometric parameters.
\textit{EnLink} adopts a locally adaptive distance metric and eliminates the
need of parameter tuning through automation. The original SNN relies only on
human input for parameter tuning so we modified SNN to run in two stages. We
first ran the original SNN 7,000 times, each with a randomly generated sample
according to within-source co-variance matrices provided in \textit{Gaia} DR2
and random parameter values within reasonable ranges. During the second stage,
we modified SNN to identify the most repeating stellar groups from 25,798 we
obtained in the first stage. We reveal 21 spatially- and kinematically-coherent
groups in the Orion complex, 12 of which previously unknown. The groups show a
wide distribution of distances extending as far as about 150 pc in front of the
star-forming Orion molecular clouds, to about 50 pc beyond them where we find,
unexpectedly, several groups. Our results expose to view the wealth of
sub-structure in the OB association, within and beyond the classical Blaauw
Orion OBI sub-groups. A full characterization of the new groups is of the
essence as it offers the potential to unveil how star formation proceeds
globally in large complexes such as Orion. The data and code that generated the
groups in this work as well as the final table can be found at \protect\url{
https://github.com/BoquanErwinChen/GaiaDR2_Orion_Dissection}.
