  Within scientific and real life problems, classification is a typical case of
extremely complex tasks in data-driven scenarios, especially if approached with
traditional techniques. Machine Learning supervised and unsupervised paradigms,
providing self-adaptive and semi-automatic methods, are able to navigate into
large volumes of data characterized by a multi-dimensional parameter space,
thus representing an ideal method to disentangle classes of objects in a
reliable and efficient way. In Astrophysics, the identification of candidate
Globular Clusters through deep, wide-field, single band images, is one of such
cases where self-adaptive methods demonstrated a high performance and
reliability. Here we experimented some variants of the known Neural Gas model,
exploring both supervised and unsupervised paradigms of Machine Learning for
the classification of Globular Clusters. Main scope of this work was to verify
the possibility to improve the computational efficiency of the methods to solve
complex data-driven problems, by exploiting the parallel programming with GPU
framework. By using the astrophysical playground, the goal was to
scientifically validate such kind of models for further applications extended
to other contexts.
