Augmented intuitive dissimilarity metric for clustering of Web user sessions. (August 2017)

• Record Type: Journal Article
• Authors: Sisodia, Dilip Singh; Verma, Shrish; Vyas, Om Prakash
• Is Part Of: Journal of information science. Volume 43:Number 4(2017)
• Page Start: 480
• Page End: 491
• Publication Date: 2017-08
• Subjects: Augmented Web user sessions -- frequency of the page -- hierarchical structure -- intuitive dissimilarity metric -- page relevance -- page duration -- syntactic structure; Information science -- Periodicals; Information science; Periodicals; 020.5
• DOI: 10.1177/0165551516648259
• Languages: English
• ISSNs: 0165-5515

Abstract

Clustering is a very useful technique to categorise Web users with common browsing activities, access patterns and navigational behaviour. Web user clustering is used to build Web visitor profiles that make the core of a personalised information recommender system. These systems are used to comprehend Web users surfing activities by offering tailored content to Web users with similar interests. The principle objective of Web user sessions clustering is to maximise the intra-group while minimising the inter-group similarity. Efficient clustering of Web users' sessions not only depend on the clustering algorithm's nature but also depend on how well user concerns are captured and accommodated by the dissimilarity measure that are used. Determining the right dissimilarity measure to capture the access behaviour of the Web user is very significant for substantial clustering. In this paper, an intuitive dissimilarity measure is presented to estimate a Web user's concern from augmented Web user sessions. The proposed usage dissimilarity measure between two Web user sessions is based on the accessing page relevance, the syntactic structure of page URL and hierarchical structure of the website. This proposed intuitive dissimilarity measure was used with K-Medoids Clustering algorithm for experimentation and results were compared with other independent dissimilarity measures. The worth of the generated clusters were evaluated by two unsupervised cluster validity indexes. The experimental results show that intuitive augmented session dissimilarity measure is more realistic and superior as compared to the other independent dissimilarity measures regarding cluster validity indexes.