In this paper, a dynamic evidential clustering algorithm (DEC) is introduced to address the computational burden of existing methods. To derive such a solution, an FCM-like objective function is first employed and minimized to obtain the support levels of the real singletons (specific) clusters to which the query objects belong, and then the query objects initially adaptively assigned to the outlier, precise or imprecise one via a new rule-based on the conflicts between the different support levels. For each imprecise object, it is finally reassigned to the singleton clusters or related meta-cluster by partial credal redistribution with the corresponding dynamic edited framework to reduce the computational burden. The proposed method can reduce the complexity to the level similar to that of the fuzzy and possibilistic clustering, which can effectively extend the application of evidential clustering, especially in big data. The effectiveness of the DEC method is tested by four experiments with artificial and real datasets.