1202.0077 (Giuliano Armano et al.)

An Interacting Particle Model for Clustering Euclidean Datasets    [PDF]

Giuliano Armano, Marco Alberto Javarone

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.

View original: http://arxiv.org/abs/1202.0077