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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.