Gaussian Mixture Models Algorithm Based on Density Peaks Clustering

• Published in: Ji suan ji ke xue Vol. 48; no. 10; pp. 191 - 196
• Main Authors: Wang, Wei-dong, Xu, Jin-hui, Zhang, Zhi-feng, Yang, Xi-bei
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.10.2021; Editorial office of Computer Science
• Subjects: Algorithms; Clustering; Datasets; Density; density peaks clustering|relative entropy|gaussian mixture models|expectation maximization algorithm|clustering algorithm; Iterative methods; Normal distribution; Optimization; Parameter estimation; Probabilistic models; Robustness
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.200800191

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 da