Dynamic classifier ensemble selection (DCES) is an important field in the machine learning. However, computational complexity of the current methods is very high. In order to solve the problem and improve the performance further, cluster based dynamic classifier ensemble selection (CDCES) is proposed in this paper. Using the proposed method to cluster the testing sample, the degree of DCES is reduced enormously and the computation complexity is decreased. At the same time, CDCES is a more general method and the traditional static ensemble selection and dynamic classifier is a specific case of the proposed method, so CDCES is more robust. Compared with the other algorithms on UCI data set, it is demonstrated that the proposed method is a more effective and lower computational complexity method.