  In recent years, the problem of fuzzy clustering has been widely concerned.
The membership iteration of existing methods is mostly considered globally,
which has considerable problems in noisy environments, and iterative
calculations for clusters with a large number of different sample sizes are not
accurate and efficient. In this paper, starting from the strategy of
large-scale priority, the data is fuzzy iterated using granular-balls, and the
membership degree of data only considers the two granular-balls where it is
located, thus improving the efficiency of iteration. The formed fuzzy
granular-balls set can use more processing methods in the face of different
data scenarios, which enhances the practicability of fuzzy clustering
calculations.
