  In the last years, Astroinformatics has become a well defined paradigm for
many fields of Astronomy. In this work we demonstrate the potential of a
multidisciplinary approach to identify globular clusters (GCs) in the Fornax
cluster of galaxies taking advantage of multi-band photometry produced by the
VLT Survey Telescope using automatic self-adaptive methodologies. The data
analyzed in this work consist of deep, multi-band, partially overlapping images
centered on the core of the Fornax cluster. In this work we use a Neural-Gas
model, a pure clustering machine learning methodology, to approach the GC
detection, while a novel feature selection method ($\Phi$LAB) is exploited to
perform the parameter space analysis and optimization. We demonstrate that the
use of an Astroinformatics based methodology is able to provide GC samples that
are comparable, in terms of purity and completeness with those obtained using
single band HST data (Brescia et al. 2012) and two approaches based
respectively on a morpho-photometric (Cantiello et al. 2018b) and a PCA
analysis (D'Abrusco et al. 2015) using the same data discussed in this work.
