This paper presents an improved dynamical k-means clustering model to solve the dynamical problem, called Dynamical K-means algorithm, in order to solve the problem that only solving the constant clustering problems of classical k-means clustering method. Based on classical k-means method, by analysis and solving the new adding samples of dynamical training data set, local renew or global clustering is performed by the changing range of objective function, and the dynamical data are clustered online. The speed of classical k-means algorithm is slow by the reiterative clustering is needed of every online clustering step, but the speed of Dynamical K-means algorithm is accelerated. Simulation results on standard and artificial social network datasets demonstrate that comparing with classical k-means clustering means, the excellent clustering results can be obtained by this method and the concept drifting phenomenon can be monitored efficiently.