In terms of the problems that the fuzzy C-means algorithm is sensitive to the initial center

easy to fall into the local optimal solution

and the algorithm iteration speed is slow

a rapid global center fuzzy clustering system model is established according to the global center theory of fuzzy clustering

and the relevant theoretical analysis and algorithm process is given. In the model

the initial centroid is determined by the DKC value scheme

and the self-defined optimization function is proposed based on the AM metric. According to this function

the cluster centers are dynamically added one by one to every stage of algorithm operation until the algorithm converges. Through experimental comparison and verification

the process reduces the influence of random selection of cluster centers on clustering results

and jumps out of local optimal solution

reduces computation

and has higher clustering accuracy and faster convergence speed.