We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Article:A New Web Usage Mining Approach for Next Page Access Prediction

by A.Anitha
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 8 - Number 11
Year of Publication: 2010
Authors: A.Anitha
10.5120/1252-1700

A.Anitha . Article:A New Web Usage Mining Approach for Next Page Access Prediction. International Journal of Computer Applications. 8, 11 ( October 2010), 7-10. DOI=10.5120/1252-1700

@article{ 10.5120/1252-1700,
author = { A.Anitha },
title = { Article:A New Web Usage Mining Approach for Next Page Access Prediction },
journal = { International Journal of Computer Applications },
issue_date = { October 2010 },
volume = { 8 },
number = { 11 },
month = { October },
year = { 2010 },
issn = { 0975-8887 },
pages = { 7-10 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume8/number11/1252-1700/ },
doi = { 10.5120/1252-1700 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:57:04.230622+05:30
%A A.Anitha
%T Article:A New Web Usage Mining Approach for Next Page Access Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 8
%N 11
%P 7-10
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

To engage users of a website at an early stage of surfing, a novel web access recommendation system is essential. In this paper, a new web usage mining approach is proposed to predict next page access. It is proposed to identify similar access patterns from web log using pair-wise nearest neighbor based clustering and then sequential pattern mining is done on these patterns to determine next page accesses. The tightness of clusters is improved by setting similarity threshold while forming clusters. In traditional recommendation models, clustering by non-sequential data decreases recommendation accuracy. In this paper it is proposed to integrate Markov model based sequential pattern mining with clustering. A variant of Markov model called dynamic support pruned all kth order Markov model is proposed in order to reduce state space complexity. Mining the web access log of users of similar interest provides good recommendation accuracy. Hence, the proposed model provides accurate recommendations with reduced state space complexity.

References
  1. Pasi Franti,Olli Virmajoki, and Ville Hautamaki “Fast Agglomerative Clustering Using a k Nearest Neighbor graph”,IEEE transaction on pattern analysis and machine intelligence.Vol 28,No 11. November 2006, pp 1875-1880
  2. Pasi Franti, Timo Kaukoranta,Day-Fann Shen and Kuo-Shu Chang “Fast and Memory Efficient Implementation of exact PNN”,IEEE Transaction on image processing,Vol 9,No 5,May 2000.pp 773-777
  3. Mathias G´ ery, Hatem Haddad,” Evaluation of Web Usage Mining approaches for user’s next request prediction” WIDM ’03 Boston, USA,ACM
  4. Siripom chimphlee,Naomie Salim,Mohd Salihin Bin Ngadiman, Witcha,Surat ,”Rough Sets Clustering and Markov Model for Web Access Prediction” ,Proceedings of post graduate annual seminar 2006, pp. 470-474
  5. Devanshu Dhyani, Sourav S Bhowmick, Wee-Keong Ng, ”Modelling and predicting web page accesses using Markov Processes”, IEEE,Computer Society, 2003,1529-4188
  6. Faten Khalil, Jiuyong Li,Hua Wang, “Integerating Recommendation Models for Improved Web Page Prediction Accuracy”, Australian Computer Society, 2008,Conferences in Research and Practice in Information Technology, Vol 74.
  7. Bamshad Mobasher, Honghua Dai, Tao Luo, Miki Nakagawa,”Effective Personalizaion based on association rule discovery from Web Usage Data”, ACM workshop on Web Information and Data management, Nov 2001.
  8. Mukund Deshpande and George Karypis, “Selective markov model for predicting web-page accesses”,Army High performance Computing Research Center, pp.1-15
  9. Faten Khalil, Jiuyong Li,Hua Wang, ” Integrating Markov model with clustering for predicting web page accesses”, Australian Conference, Mar 2007 ,pp 1-26
Index Terms

Computer Science
Information Sciences

Keywords

Similarity measure pair-wise nearest neighbor Markov model