An Efficient Approach for Clustering Web Access Patterns from Web Logs

Abstract

The interests of web users can be revealed by their visited web pages and time duration on these web pages during their surfing. Time duration on a web page is characterized as a fuzzy linguistic variable because linguistic variable makes users easily understand the expression of time duration and can disregard subtle difference between two time durations. Each web access pattern from web logs is transformed as corresponding fuzzy web access pattern, which is a fuzzy vector composed of fuzzy linguistic variables or 0. Each element in fuzzy web access patterns represents visited web page and time duration on this web page. This paper proposed a rough k-means clustering algorithm based on properties of rough variable to group the gained fuzzy web access patterns. Finally, an example and experiment is provided to illustrate the clustering process. Using this approach, users can effectively mine web logs records to discover interesting user access patterns.