Clustering-based web page prediction. (30th January 2012)

• Record Type: Journal Article
• Authors: Dutta, Ruma; Kundu, Anirban; Mukhopadhyay, Debajyoti
• Is Part Of: International journal of knowledge and web intelligence. Volume 2:Number 4(2011)
• Page Start: 257
• Page End: 271
• Publication Date: 2012-01-30
• Subjects: web page prediction -- HITS algorithm -- clustering -- cellular automata -- CA; Information retrieval -- Periodicals; Web site development -- Periodicals; Internet programming -- Periodicals; 004.67805
• Languages: English
• ISSNs: 1755-8255

Abstract

Web page prediction plays an important role by predicting and fetching probable web page of next request in advance, resulting in reducing the user latency. The users surf the internet either by entering URL or search for some topic or through link of same topic. For searching and for link prediction, clustering plays an important role. Besides the topic, navigational behaviour is not ignored. This paper proposes a web page prediction model giving significant importance to the user's interest using the clustering technique and the navigational behaviour of the user through Markov model. The clustering technique is used for the accumulation of the similar web pages. Similar web pages of same type reside in the same cluster, the cluster containing web pages have the similarity with respect to topic of the session. The clustering algorithms considered are K-means and K-mediods, where K is determined by HITS algorithm. Finally, the predicted web pages are stored in form of cellular automata to make the system more memory efficient.