This paper describes our analysis of using extra constraint terms regarding relations between cluster prototypes in possibilistic c-shell clustering. The extra constraints are implemented as additional terms in the cost function. This allows users of these algorithms to incorporate additional knowledge regarding properties cluster prototype into the clustering process. Our analysis here focuses on the use of one extra term for locating circles (shell clustering with circular prototypes) with similar radii. An adjustable factor is used to control the strength of this constraint. For possibilistic clustering, this couples the update procedure of the otherwise independent prototypes. Our experiments, using both simulation and real image data, indicate that this is especially useful in locating actual clusters when the available data are noisy.