A Flexible Possibilistic C-Template Shell Clustering Method with Adjustable Degree of Deformation

Abstract

This paper presents a new method of template based shell clustering that allows more flexible free deformation of the cluster prototypes with respect to the template-defined shapes. This is achieved via a soft division of the template into several template parts, each allowed to have its own set of transform parameters. A fuzzification factor, inspired by the one used in the standard fuzzy c-means algorithm, is used to control the degree of deformation by blending the transform parameters of the template parts. We demonstrate that this approach gives better shape detection and fitting results than the original possibilistic c-template algorithm using synthetic data of several different shapes.