Clickstream Clustering using Weighted Longest Common Subsequences

Categorizing visitors based on their interactions with a website is a key problem in web usage mining. The clickstreams generated by various users often follow distinct patterns, the knowledge of which may help in providing customized content. In this paper, we propose a novel and effective algorithm for clustering webusers based on a function of the longest common subsequence of their clickstreams that takes into account both the trajectory taken through a website and the time spent at each page. Results are presented on weblogs of www.sulekha.com to illustrate the techniques. keywords : web usage mining, clickstream, subsequence, clustering.