Web Usage Data Clustering Using Dbscan Algorithm and Set Similarities

Web usage mining is the application of data mining techniques to web log data repositories. It is used in finding the user access patterns from web access log. User page visits are sequential in nature. In this paper we presented new Rough set Dbscan clustering algorithm which identifies the behavior of the users page visits, order of occurrence of visits. Web data Clusters are formed using the rough set Similarity Upper Approximations. We present the experimental results on MSNBC web navigation dataset, and proved that Rough set Dbscan clustering has better efficiency and performance clustering in web usage mining is finding the groups which share common interests compared to Rough set agglomerative clustering.