An Interacting Particle Model for Clustering Euclidean Datasets

In this paper we propose a method based on interacting particle physics, devised for clustering Euclidean datasets without initial constraints or conditions. We model any dataset as an interacting particle system, whose elements correspond to particles that interact through a simplified version of Lennard-Jones potentials. In so doing, mutual attractive interactions allow to identify groups of proximal particles. The main outcome of this modeling task is an adjacency matrix, taken as input by a community detection algorithm aimed to identify different partitions. The underlying conjecture is that, using a multiresolution analysis, the adopted model allows to find the right number of clusters for any given dataset. Experimental results, performed in comparison with a classical clustering algorithm, confirm this assumption.