  The publication of the Gaia Data Release 2 (Gaia DR2) opens a new era in
Astronomy. It includes precise astrometric data (positions, proper motions and
parallaxes) for more than $1.3$ billion sources, mostly stars. To analyse such
a vast amount of new data, the use of data mining techniques and machine
learning algorithms are mandatory. The search for Open Clusters, groups of
stars that were born and move together, located in the disk, is a great example
for the application of these techniques. Our aim is to develop a method to
automatically explore the data space, requiring minimal manual intervention. We
explore the performance of a density based clustering algorithm, DBSCAN, to
find clusters in the data together with a supervised learning method such as an
Artificial Neural Network (ANN) to automatically distinguish between real Open
Clusters and statistical clusters. The development and implementation of this
method to a $5$-Dimensional space ($l$, $b$, $\varpi$, $\mu_{\alpha^*}$,
$\mu_\delta$) to the Tycho-Gaia Astrometric Solution (TGAS) data, and a
posterior validation using Gaia DR2 data, lead to the proposal of a set of new
nearby Open Clusters. We have developed a method to find OCs in astrometric
data, designed to be applied to the full Gaia DR2 archive.
