结合EM和DBSCAN算法对网站用户行为进行建模

M. Udantha, Surangika Ranathunga, G. Dias
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引用次数: 3

摘要

如果对Web日志进行适当的分析,可以提供有关相应网站的用户访问模式的大量信息。然而,由于日志量大,以及日志文件在集群环境中的分布,查找隐藏在低级日志数据中的有趣模式并非易事。本文提出了一种新颖的技术,以迭代的方式应用基于密度的空间聚类(DBSCAN)和期望最大化(EM)算法对web用户会话进行聚类。每个集群对应一个或多个web用户活动。通过频繁模式挖掘和顺序模式挖掘技术,识别出每个集群的唯一用户访问模式。与EM、DBSCAN和k-means算法的聚类输出相比,该技术在web会话挖掘中显示出更高的准确性,并且在识别聚类随时间变化的情况下更有效。我们证明了所实现的系统不仅能够识别常见的用户行为,而且能够识别网络攻击。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modelling website user behaviors by combining the EM and DBSCAN algorithms
Web logs can provide a wealth of information on user access patterns of a corresponding website, when they are properly analyzed. However, finding interesting patterns hidden in the low-level log data is non-trivial due to large log volumes, and the distribution of the log files in cluster environments. This paper presents a novel technique, the application of Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Expectation Maximization (EM) algorithms in an iterative manner for clustering web user sessions. Each cluster corresponds to one or more web user activities. The unique user access pattern of each cluster is identified by frequent pattern mining and sequential pattern mining techniques. When compared with the clustering output of EM, DBSCAN, and k-means algorithms, this technique shows better accuracy in web session mining, and it is more effective in identifying cluster changes with time. We demonstrate that the implemented system is capable of not only identifying common user behaviors, but also of identifying cyber-attacks.
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