This paper presents a method for segmenting the web users based on their web access patterns. History of web pages visited by users includes informations like access sequences of web users and number of visits of web pages and reveals interest of users in particular pages. The web users’ access patterns can be segmented to group the users with similar interests. In this work, a simple, count based technique is used for preprocessing web access data so as to convert it into a database with fixed number of attributes. A novel approach based on fuzzy clustering principles for unsupervised clustering is extended to identify the number of web user groups based on their access patterns. This method starts by assuming that all the data points are initial clusters. Pairs of similar clusters are then merged based on fuzzy membership values. This paper also compare the cluster count obtained with this approach with the cluster count obtained with Cohonen’s unsupervised clustering algorithm. The tools available with IBM SPSS Modeler 14.1 are used to benchmark the quality of cluster estimation.

Fuzzy Clustering, Web access pattern, Fuzzy Logic, k-means clustering, c-means clustering

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