CLIQUE: Clustering based on density on web usage data: Experiments and test results

Abstract

Clustering web sessions is to group web sessions based on similarity and consists of minimizing the intra-group similarity and maximizing the inter-group similarity. The other question that arises is how to measure similarity between web sessions. Here in this paper we adopted a CLIQUE (CLUstering in QUEst) algorithm for clustering web sessions for web personalization. Then we adopted various similarity measures like Euclidean distance, projected Euclidean distance Jaccard, cosine and fuzzy dissimilarity measures to measure the similarity of web sessions using sequence alignment to determine learning behaviors. This has significant results when comparing similarities between web sessions with various measures, we performed a variety of experiments in the context of density based clustering, based on sequence alignment to measure similarities between web sessions where sessions are chronologically ordered sequences of page visits.