Effective Keyword and Similarity Thresholds for the Discovery of Themes From the User Web Access Patterns

Clustering techniques have been used by many intelligent software agents to group similar access patterns of the Web users into high level themes which express users intentions and interests. However, such techniques have been mostly focusing on one salient feature of the Web document visited by the user, namely the extracted keywords. The major aim of these techniques is to come up with an optimal threshold for the number of keywords needed to produce more focused themes. In this paper the authors focus on both keyword and similarity thresholds to generate themes with concentrated themes, and hence build a more sound model of the user behavior.