  Kinetic turbulence in magnetized space plasmas has been extensively studied
via in situ observations, numerical simulations and theoretical models. In this
context, a key point concerns the formation of coherent current structures and
their disruption through magnetic reconnection. We present automatic techniques
aimed at detecting reconnection events in large data set of numerical
simulations. We make use of clustering techniques known as K-means and DBscan
(usually referred in literature as unsupervised machine learning approaches),
and other methods based on thresholds of standard reconnection proxies. All our
techniques use also a threshold on the aspect ratio of the regions selected. We
test the performance of our algorithms. We propose an optimal aspect ratio to
be used in the automated machine learning algorithm: AR=18. The performance of
the unsupervised approach results to be strongly competitive with respect to
those of other methods based on thresholds of standard reconnection proxies.
