Mining web browsing patterns for E-commerce

Web user clustering, Web page clustering, and frequent access path recognition are important issues in E-commerce. They can be used for the purposes of marketing strategies and product offerings, mass customization and personalization, and Web site adaptation. In this paper, we view the topology of a Web site as a directed graph, and use a user's access information on all URLs of a Web site as features to characterize the user and use all users' access information on a URL as features to characterize the URL. The user clusters and Web page clusters are discovered by both vector analysis and fuzzy set theory based methods. The frequent access paths are recognized based on Web page clusters and take into account the underlying structure of a Web site. Our method does not require the identification of user sessions from Web server logs, and both a user and a page can be assigned to more than one cluster. Our frequent access path identification algorithm is not based on sequential pattern mining, so it avoids the performance difficulties of the latter. We applied our algorithms to five real world data sets of different sizes. Our results show the effectiveness of the proposed algorithms with the fuzzy set theory based methods being slightly more accurate. (C) 2006 Elsevier B.V. All rights reserved.