Automating the Superparamagnetic Clustering Method
Agrawal, H. and Domany, E.
Department of Physics of Complex Systems, Weizmann Institute of Science
Abstract:
Superparamagnetic clustering is a method for clustering data that exploits
the phase transitions in grannular ferromagnets for solving the clustering
problem. Each data point is associated with a spin; the mapping from the
clustering problem to a ferromagnet is dependent on the value of a parameter
'K', which controls the number of neighbors with which each spin interacts.
The value of K determines the kind of highly inhomogeneous lattice to which
the data are transformed, and the solution of the clustering process exhibits
non-trivial dependence on this parameter. Untill recently K was determined by
exploring a wide interval of possible values, which is a computationally
expensive procedure. We present a method for determining the range of this
parameter for which best clustering solutions are obtained. The method is
fully automated and gives the optimal range of 'K' almost instantaneously.