Gaussian Mixture Models Algorithm Based on Density Peaks Clustering

Abstract

Due to the existence of a large number of sample data which obey the Gaussian distribution,GMM (Gaussian mixture models) is used to cluster these sample data and get more accurate clustering results.In general,EM algorithm(expectation maxi-mization algorithm) is used to estimate the parameters of GMM iteratively.However,the traditional EM algorithm has two shortcomings:it is sensitive to the initial clustering center;the itera-tive termination condition of iterative parameter estimation is to judge that the distance between two adjacent estimated parameters is less than a given threshold,which can't guarantee that the algorithm converges to the optimal value of the parameters.In order to overcome the above shortcomings,density peaks clustering (DPC) is proposed to initialize EM algorithm to improve the robustness of the algorithm.The relative entropy is used as the ite-ration termination condition of the EM algorithm to optimize the parameters of GMM algorithm.The comparative experiments on artificial datasets and UCI datasets show that the new algorithm not only improves the robustness of EM algorithm,but also outperforms the traditional clustering algorithm.On the datasets which obey Gaussian distribution,the new algorithm greatly improves the clustering accuracy.

Keywords

• density peaks clustering|relative entropy|gaussian mixture models|expectation maximization algorithm|clustering algorithm