  In this paper we propose a framework inspired by interacting particle physics
and devised to perform clustering on multidimensional datasets. To this end,
any given dataset is modeled as an interacting particle system, under the
assumption that each element of the dataset corresponds to a different particle
and that particle interactions are rendered through gaussian potentials.
Moreover, the way particle interactions are evaluated depends on a parameter
that controls the shape of the underlying gaussian model. In principle,
different clusters of proximal particles can be identified, according to the
value adopted for the parameter. This degree of freedom in gaussian potentials
has been introduced with the goal of allowing multiresolution analysis. In
particular, upon the adoption of a standard community detection algorithm,
multiresolution analysis is put into practice by repeatedly running the
algorithm on a set of adjacency matrices, each dependent on a specific value of
the parameter that controls the shape of gaussian potentials. As a result,
different partitioning schemas are obtained on the given dataset, so that the
information thereof can be better highlighted, with the goal of identifying the
most appropriate number of clusters. Solutions achieved in synthetic datasets
allowed to identify a repetitive pattern, which appear to be useful in the task
of identifying optimal solutions while analysing other synthetic and real
datasets.
