The fuzzy c-Means algorithm is a kind of iterative algorithms based on greedy algorithms.It converges to a local minimum value along the iteration sequence

yet it has the insufficient searching ability and can easily fall into local optimum solution.This paper

based on tabu search

introduces a fuzzy clustering algorithm.It uses tabu search in a solution's neighborhood and adopts the tabu strategy of long-term tabu lists based on the local convergence of FCM

which guarantees to move the search starting point constantly and avoids repeated searching.In addition

chaos optimization and dynamic step strategies are utilized to strengthen its global search ability in order to achieve global optimal solution.Experimental results show that this algorithm improves the accuracy of clustering considerably and has great stability.Compared with group-wise algorithm and genetic algorithm

this algorithm also has some advantages.