融合密度峰值的高斯混合模型聚类算法

针对高斯混合模型（GMM）聚类算法对初始值敏感且容易陷入局部极小值的问题，利用密度峰值（DP）算法全局搜索能力强的优势，对GMM算法的初始聚类中心进行优化，提出了一种融合DP的GMM聚类算法（DP-GMMC）。首先，基于DP算法寻找聚类中心，得到混合模型的初始参数；其次，采用最大期望（EM）算法迭代估计混合模型的参数；最后，根据贝叶斯后验概率准则实现数据点的聚类。在Iris数据集下，DP-GMMC聚类准确率可达到96.67%，与传统GMM算法相比提高了33.6个百分点，解决了对初始聚类中心依赖的问题。实验结果表明，DP-GMMC对低维数据集有较好的聚类效果。

Clustering algorithm of Gaussian mixture model based on density peaks

The clustering algorithm of Gaussian Mixture Model (GMM) is sensitive to initial value and easy to fall into local minimum. In order to solve the problems, taking advantage of strong global search ability of Density Peaks (DP) algorithm, the initial clustering center of GMM algorithm was optimized, and a new Clustering algorithm of GMM based on DP (DP-GMMC) was proposed. Firstly, the clustering center was searched by the DP algorithm to obtain the initial parameters of mixed model. Then, the Expectation Maximization (EM) algorithm was used to estimate the parameters of mixed model iteratively. Finally, the data points were clustered according to the Bayesian posterior probability criterion. In the Iris data set, the problem of dependence on the initial clustering center is solved, and the clustering accuracy of DP-GMMC can reach 96.67%, which is 33.6 percentage points higher than that of the traditional GMM algorithm. The experimental results show that, the proposd DP-GMMC has better clustering effect on low-dimensional datasets.