Robust c-shells based deterministic annealing clustering algorithm

A new clustering method, robust c-shells based deterministic annealing (RCSDA) algorithm is developed. This development recasts the concept of fuzzy c-shells algorithm into the probability framework and offers several improved features over existing clustering algorithms. First, it is a global or close-to-global minimization algorithm through deterministic annealing rather than a local minimization method in the original fuzzy c-shells approach. Second, it is more effective in boundary detection with compact or hollow spherical shells compared to the original deterministic annealing approach. Finally, the basic idea of Dave\´s "noise clustering" is introduced into the algorithm which makes it robust against noise. The superiority of the proposed clustering method is supported by experimental results.