  We statistically evaluate and compare four orbital similarity criteria within
five-dimensional parameter space ($D_{SH}$, $D_D$, $D_H$, and $\varrho_2$) to
study dynamical associations using the already classified meteors (manually by
a human) in CAMS database as a benchmark. In addition, we assess various
distance metrics typically used in Machine Learning with two different vectors:
ORBIT, grounded in heliocentric orbital elements, and GEO, predicated on
geocentric observational parameters. Additionally, we compute the optimal
cut-offs for all methods for distinguishing sporadic background events. Our
findings demonstrate the superior performance of the sEuclidean metric in
conjunction with the GEO vector. Within the scope of D-criteria, $D_{SH}$
emerged as the preeminent metric, closely followed by $\varrho_2$. $\varrho_2$
stands out as the most equivalence to the distance metrics when utilizing the
GEO vector and the most compatible with GEO and ORBIT simultaneously, whereas
$D_D$ aligns more closely when using the ORBIT vector. The stark contrast in
$D_D$'s behavior compared to other D-criteria highlights potential
inequivalence. Geocentric features provide a more robust basis than orbital
elements for meteor dynamical association. Most distance metrics associated
with the GEO vector surpass the D-criteria when differentiating the meteoroid
background. Accuracy displayed a dependence on solar longitude with a
pronounced decrease around 180$^\circ$ matching an apparent increase in the
meteoroid background activity, tentatively associated with the transition from
the Perseids to the Orionids. Considering lately identified meteor showers,
$\sim$27\% of meteors in CAMS would have different associations. This work
unveils that Machine Learning distance metrics can rival or even exceed the
performance of tailored orbital similarity criteria for meteor dynamical
association.
