Clustering Web Access Patterns Based on learning Automata
Babak Anari, Mohammad Reza Meybodi, Zohreh Anari · 2011
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.