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Adaptive Neural Network Clustering of Web Users
April 2004 (vol. 37 no. 4)
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
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.

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.

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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