Traditionally, the flow cytometry data is analyzed manually, which is inefficient and depends on expert experiences. In recent years, a lot of automatic cluster algorithms have been proposed. However, the clustering performance is not satisfied for sparse data with a random distribution. Therefore, this paper presents an automatic clustering method based on densitydistance center for tmixture model algorithm in flow cytometry data, which is suitable for rare samples. The proposed method finds the center of each group by densitydistance center algorithm and uses it as the initial value of tmixture model to estimate the sample data by maximum likelihood estimation. Compared with the classical algorithm, the result shows that the tmixture model based on densitydistance center has better stability and reliability, and can better fit small or mixed samples.