An improved clustering algorithm based on finite Gaussian mixture model

The Finite Gaussian Mixture Model (FGMM) is the most commonly used model for describing mixed density distribution in cluster analysis. An important feature of the FGMM is that it can infinitely approximate any continuous distribution, as long as the model contains enough number of components. In the clustering analysis based on the FGMM, the EM algorithm is usually used to estimate the parameters of the model. The advantage is that the computation is stable and the convergence speed is fast. However, the EM algorithm relies heavily on the estimation of incomplete data. It does not use any information to reduce the uncertainty of missing data. To solve this problem, an EM algorithm based on entropy penalized maximum likelihood estimation is proposed. The novel algorithm constructs the conditional entropy model between incomplete data and missing data, and reduces the uncertainty of missing data through incomplete data. Theoretical analysis and experimental results show that the novel algorithm can effectively adapt to the FGMM, improve the clustering results and improve the efficiency of the algorithm.