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基于聚类分析的网络流量高斯混合模型
引用本文:程华,房一泉.基于聚类分析的网络流量高斯混合模型[J].医学教育探索,2010(2):255-260.
作者姓名:程华  房一泉
作者单位:华东理工大学计算机科学与工程系;华东理工大学计算机科学与工程系
摘    要:基于聚类算法对数据对象多个属性综合聚类的特点,研究网络流量的GMM模型及其在数据流尺度上的Lognormal分布。用EM算法研究了具有交互特征的网络流量的分类;通过与K-means算法比较,讨论了EM算法在流量聚类中的适用性;通过平衡和不平衡流量的聚类分析,研究了不同类型流量GMM建模的有效性。研究流量的幂律关系及其在不同尺度间的传递性,用户行为和应用程序特征通过传输层控制协议分解传递到IP层后,在数据包尺度上表现出分形和自相似性,在数据流尺度上表现出Log- normal分布。

关 键 词:高斯混合模型    EM算法    聚类    Log-normal分布    幂律关系

Gaussian Mixture Model of Network Traffic Based on Clustering Analysis
Abstract:The cluster algorithm may make classification on a few attributes of objects. Based on the above feature, this paper studies the Gaussian mixture model (GMM) of network traffic and its log-normal distribution on flow scale. The EM algorithm is used to cluster traffics with interactive features. It is shown that EM algorithm is more appropriate on traffic clustering than K-means algorithm. The clustering analysis on both the balanced and unbalanced traffics shows that GMM is effective on different kinds of traffics. The log normal distribution and the transitivity of power law from application layer to IP layer are studied. After the log normal distribution in application layer produced by user behaviors and application features is transferred to IP layer via the control protocols in transport layer, the traffic presents fractal and self similar on the packet scale.
Keywords:Gaussian mixture model  EM algorithm  clustering  Log-normal distribution  power law
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