To minimize the negative effects of the outliers in the training data set during the data description modeling, a support vector data description algorithm(SVDD) based on fast clustering analysis is proposed. The proposed approach consists of two stages of strategy. Firstly, a fast clustering analysis algorithm is applied to preprocessing the training data set. The outliers in the training dataset that affect the model are removed. Then, a weighted support vector data description based on ??NN is used in the second stage. Experimental results on benchmark datasets show that the performance of the present approach is superior to conventional SVDD and density-induced SVDD in accuracy.