Clustering web usage data is useful to discover interesting sequential patterns related to user traversals, behavior and their characteristics, which helps for the improvement of better search engines and Web personalization. Clustering web sessions is to group them based on similarity and consists of minimizing the intra-cluster similarity and maximizing the inter-group similarity. The other issue that arises is how to measure similarity between sequences. There exist multiple similarity measures in the past like Euclidean, Jaccard, Cosine and many. Most of the similarity measures presented in the history deals only with sequence data but not the order of occurrence of data.