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

A new method for unveiling Open Clusters in Gaia: new nearby Open Clusters confirmed by DR2