Mining Usage Profiles From Access Data Using Fuzzy Clustering

In this work, we present an approach to clustering Web site users into different groups and generating common user profiles. These profiles are intended to be used to make recommendations by suggesting interesting links to the user. By using a fuzzy clustering algorithm, we enable generation of overlapping clusters that can capture the uncertainty among Web user’s navigation behavior. Preliminary experimental results are presented to show the clusters generated by mining the access log data of a web site.