An alternative approach for clustering web user sessions considering sequential information

Abstract

Clustering is a prominent technique in data mining applications. It generates groups of data points that are similar to each other in a given aspect. Each group has some inherent latent similarity which is computed using the similarity measures. Clustering web users based on navigational pattern has always been an interesting as well as a challenging task. A web user, based on its navigational pattern, may belong to multiple categories. Intrinsically, web user navigation pattern exhibits sequential property. When dealing with sequence data, a similarity measure should be chosen, which captures both the order as well as content information during computation of similarity among sequences. In this paper, we have utilized the Sequence and Set Similarity Measure S^{3}M with rough set based similarity upper approximation clustering algorithm to group web users based on their navigational patterns. The quality of cluster formed using rough set based clustering algorithm with S^{3}M measure has been compared with the well known clustering algorithm, Density based spatial clustering of applications with noise DBSCAN. The experimental results show the viability of our approach.