The traditional fuzzy c-means clustering algorithm is easy to trap in local optimums as its sensitive selection of the initial cluster centers. For overcoming this disadvantage, this paper presents a fuzzy c-means algorithm combined an improved artificial bee colony algorithm with the strategy of rank fitness selection. The strategy is aimed to increase the selection probability of the individual with better fitness. The proposed algorithm combines the advantages of the high efficiency of fuzzy c-means algorithm and the global search ability of the artificial bee colony algorithm. The experiment and analysis results demonstrate that the algorithm can solve the optimization problem of the initial cluster centers with high robustness and better quality of clustering.