计算机科学 ›› 2021, Vol. 48 ›› Issue (10): 191-196.doi: 10.11896/jsjkx.200800191
王卫东, 徐金慧, 张志峰, 杨习贝
WANG Wei-dong, XU Jin-hui, ZHANG Zhi-feng, YANG Xi-bei
摘要： 由于存在大量服从高斯分布的样本数据,采用高斯混合模型(Gaussian Mixture Models,GMM)对这些样本数据进行聚类分析,可以得到比较准确的聚类结果。通常采用EM算法(Expectation Maximization Algorithm)对GMM的参数进行迭代式估计。但传统EM算法存在两点不足:对初始聚类中心的取值比较敏感;迭代式参数估计的迭代终止条件是相邻两次估计参数的距离小于给定的阈值,这不能保证算法收敛于参数的最优值。为了弥补上述不足,提出采用密度峰值聚类(Density Peaks Clustering,DPC)来初始化EM算法,以提高算法的鲁棒性,采用相对熵作为EM算法的迭代终止条件,实现对GMM算法参数值的优化选取。在人工数据集及UCI数据集上的对比实验表明,所提算法不但提高了EM算法的鲁棒性,而且其聚类结果优于传统算法。尤其在服从高斯分布的数据集上的实验结果显示,所提算法大幅提高了聚类精度。
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