Hunting for open clusters in \textit{Gaia} DR2: the Galactic anticentre
  The Gaia Data Release 2 (DR2) provided an unprecedented volume of precise
astrometric and excellent photometric data. In terms of data mining the Gaia
catalogue, machine learning methods have shown to be a powerful tool, for
instance in the search for unknown stellar structures. Particularly, supervised
and unsupervised learning methods combined together significantly improves the
detection rate of open clusters. We systematically scan Gaia DR2 in a region
covering the Galactic anticentre and the Perseus arm $(120 \leq l \leq 205$ and
$-10 \leq b \leq 10)$, with the goal of finding any open clusters that may
exist in this region, and fine tuning a previously proposed methodology
successfully applied to TGAS data, adapting it to different density regions.
Our methodology uses an unsupervised, density-based, clustering algorithm,
DBSCAN, that identifies overdensities in the five-dimensional astrometric
parameter space $(l,b,\varpi,\mu_{\alpha^*},\mu_{\delta})$ that may correspond
to physical clusters. The overdensities are separated into physical clusters
(open clusters) or random statistical clusters using an artificial neural
network to recognise the isochrone pattern that open clusters show in a colour
magnitude diagram. The method is able to recover more than 75% of the open
clusters confirmed in the search area. Moreover, we detected 53 open clusters
unknown previous to Gaia DR2, which represents an increase of more than 22%
with respect to the already catalogued clusters in this region. We find that
the census of nearby open clusters is not complete. Different machine learning
methodologies for a blind search of open clusters are complementary to each
other; no single method is able to detect 100% of the existing groups. Our
methodology has shown to be a reliable tool for the automatic detection of open
clusters, designed to be applied to the full Gaia DR2 catalogue.
