期刊名称：International Journal of Computer Science and Information Technologies
电子版ISSN：0975-9646
出版年度：2013
卷号：4
期号：4
页码：541-545
出版社：TechScience Publications
摘要：Users of a Web site usually perform their interestoriented actions by click-ing or visiting Web pages, which are traced in access log files. Clustering Web user access patterns may capture common user interests to a Web site, and in turn, build user profiles for advanced Web applications, such as Web caching and prefetching. The conventional Web usage mining techniques for clustering Web user sessions can discover usage patterns directly, but cannot identify the latent factors or hidden relationships among users' navigational behavior. In this paper, we propose an approach based on a vector space model, called Random indexing, to discover such intrinsic characteristics of Web users' activities. The underlying factors are then utilized for clustering individual user navigational patterns and creating common user profiles. The clustering results will be used to predict and prefetch Web requests for grouped users. We demonstrate the usability and superiority of the proposed Web user clustering approach through experiments on a real Web log file. The clustering and prefetching tasks are evaluated by comparison with previous studies demonstrating better clustering performance and higher prefetching accuracy.
关键词：Web user clustering; User behavior; Random;Indexing; Web prefetching.
