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Issue No.04 - April (2004 vol.37)
pp: 34-40
Santosh K. Rangarajan , Louisiana Tech University
Vir V. Phoha , Louisiana Tech University
Kiran S. Balagani , Louisiana Tech University
Rastko R.Selmic , Louisiana Tech University
S.S. Iyengar , Louisiana State University
ABSTRACT
Web server access logs contain substantial data about user access patterns, which can enhance the degree of personalization that a Web site offers. Restructuring a site to individual user interests increases the computation at the server to an impractical degree, but organizing according to user groups can improve perceived performance. <p>An unsupervised clustering algorithm based on adaptive resonance theory adapts to changes in users' access patterns over time without losing earlier information. The algorithm outperformed the traditional k-means clustering algorithm in terms of intracluster distances. A prefetching application based on the algorithm achieved a hit accuracy rate for Web site page requests ranging from 82.05 to 97.78 percent.</p>
CITATION
Santosh K. Rangarajan, Vir V. Phoha, Kiran S. Balagani, Rastko R.Selmic, S.S. Iyengar, "Adaptive Neural Network Clustering of Web Users", Computer, vol.37, no. 4, pp. 34-40, April 2004, doi:10.1109/MC.2004.1297299
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