文章基本信息

标题：Clustering Web Access Patterns Based on Learning Automata
作者：Babak Anari ; Mohammad Reza Meybodi ; Zohreh Anari 等
期刊名称：International Journal of Computer Science Issues
印刷版ISSN：1694-0784
电子版ISSN：1694-0814
出版年度：2011
卷号：8
期号：5
出版社：IJCSI Press
摘要：The interest of web users can be revealed by the visited web pages and time duration on these web pages during their surfing. In this paper a new method based on Learning Automata for clustering web access patterns is proposed. At the first step of the proposed algorithm, each web access pattern from web logs is transformed into a weight vector using the learning automata. In the second step a primitive clustering is performed to group weight vectors into a number of clusters. Finally, the weighted Fuzzy c-means approach is developed to deal with the results of the second step. Our experiments on a large real data set show that the method is efficient and practical for web mining applications.
关键词：Web access patterns; Clustering; Learning automata; Distributed learning automata; Time duration