关键词: 模糊C均值, 期望最大化算法, 无监督聚类, 混合高斯模型

Abstract: Aiming at the problem that the hybrid Gaussian model using the traditional expectation-maximum algorithm for parameter estimation was too dependent on the initial probability density center for the final clustering effect, we proposed an improved expectation-maximum  algorithm based on the fuzzy C-means algorithm for parameter initialization. The  experimental results show that compared with the traditional unsupervised clustering algorithms (the fuzzy C-means algorithm, the K-means algorithm and the unmodified expectation-maximum algorithm),  the improved expectation-maximum algorithm has better clustering performance metrice and  better global clustering effect than  the traditional clustering algorithm in a practical user knowledge level clustering task.

Key words: fuzzy C-means, expectation-maximum algorithm, unsupervised clustering, Gaussian mixture model