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基于半监督聚类的Web流量分类 被引量：3

Web Traffic Classification Based on Semi-supervised Clustering
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摘要 提出了一种基于半监督学习的方法对Web流量进行聚类分析,使用隐马尔可夫模型对用户流量进行描述和聚类分析。该方法通过对少量数据进行人工标识,利用已标识数据对无监督聚类结果进行调整,以得到与人工分类匹配的聚类结果。使用真实的Web流量对提出的方法进行验证,实验结果表明该方法能有效地对Web流量进行分类,并得到相应的描述模型。 This paper presented a Web traffic classification method based on semi-supervised clustering, which uses HMM （Hidden Markov Model） to model and analyze Web client traffic. The method first runs an unsupervised clustering process on the whole data set,and then uses the pre-labeled data to adjust the result clusters. The paper also presented the experiment result on real network data to validate the purposed method.
作者 陆伟宙 余顺争
机构地区 中山大学电子与通信工程系
出处 《计算机科学》 CSCD 北大核心 2009年第2期90-94,共5页 Computer Science
基金 国家高技术研究发展计划(863)资助项目(批准号:2007AA01Z449) 国家自然科学基金-广东联合基金重点项目(U0735002) 国家自然科学基金项目(90304011)资助
关键词 半监督聚类 隐马尔可夫模型 WEB流量 Semi-supervised clustering, Hidden markov model, Web traffic
分类号 TP391 [自动化与计算机技术—计算机应用技术] O212.4 [理学—概率论与数理统计]
  • 相关文献

参考文献10

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二级参考文献9

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共引文献52

同被引文献23

  • 1俞研,黄皓.一种半聚类的异常入侵检测算法[J].计算机应用,2006,26(7):1640-1642. 被引量：17
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引证文献3

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计算机科学
2009年 第2期

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