Issue No. 04 - April (2004 vol. 37)
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. <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>
V. V. Phoha, K. S. Balagani, R. R.Selmic, S. Iyengar and S. K. Rangarajan, "Adaptive Neural Network Clustering of Web Users," in Computer, vol. 37, no. , pp. 34-40, 2004.