关键词: 网络流分类, t分布混合模型, 期望最大化算法, 半监督分类

Abstract: Traditional Gaussian distribution is susceptible to the influence of edge points and outliers in data samples, therefore student’s t-distribution is adopted to improve Gaussian distribution. The EM（Expectation Maximization） algorithm is used to build T-distribution Mixture Model（TMM） for network multimedia traffic dataset. Then, A new Limited T-distribution Mixture Model（LTMM） is presented to reduce the number of iterations for EM algorithm, whose effectiveness is demonstrated by theoretical analysis and experiments. The flows of multimedia services are classified in this paper. Experiments show that the proposed algorithm can achieve higher accuracy than existing methods and the fitted model is better than K-Means algorithm and EM algorithm for Gaussian mixture model.

Key words: internet traffic classification, t-distribution mixture model, Expectation Maximization（EM） algorithm, semi-supervised learning