Scale-based clustering using the radial basis function network

Abstract

Adaptive learning dynamics of the radial basis function network (RBFN) are compared with a scale-based clustering technique and a relationship between the two is pointed out. Using this link, it is shown how scale-based clustering can be done using the RBFN, with the radial basis function (RBF) width as the scale parameter. The technique suggests the "right" scale at which the given data set must be clustered and obviates the need for knowing the number of clusters beforehand. We show how this method solves the problem of determining the number of RBF units and the widths required to get a good network solution.<>