Abstract: Finite mixture model is an effective clustering method based on probability model． Aiming at the clustering algorithm of Gaussian mixture model.This paper imposed entropy penalized operators on the mixed coefficients of components and the labels of samples respectively,which brings to two levels controls for the number of components and rapid reduction of the illegitimate ones．Thus the algorithm converges to exact solutions with only a few iterations．Since the traditional algorithm is very sensitive to the initial values (for example,the number of components must be set in advance),which often leads to the EM algorithm to fall into local optima or converges to the boundary of the solution space,the new algorithm of this paper is very robust and has no special demands for the initializations,just testified by the experiments.

Key words: Gaussian finite mixture model,Clustering,Entropy,EM algorithm