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Communications of the ACM
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IEEE Transactions on Knowledge and Data Engineering
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Machine Learning
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ACM Transactions on Internet Technology (TOIT)
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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ADL '98 Proceedings of the Advances in Digital Libraries Conference
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KDEX '99 Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange
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ACM SIGKDD Explorations Newsletter
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ICDCS '96 Proceedings of the 16th International Conference on Distributed Computing Systems (ICDCS '96)
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Web usage mining: discovery and application of interesting patterns from web data
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Information Sciences—Informatics and Computer Science: An International Journal - special issue: Knowledge discovery from distributed information sources
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Information Sciences—Informatics and Computer Science: An International Journal - Special issue: Informatics and computer science intelligent systems applications
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Data & Knowledge Engineering
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IEEE Transactions on Fuzzy Systems
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Proceedings of the Symposium on Human Interface 2009 on Human Interface and the Management of Information. Information and Interaction. Part II: Held as part of HCI International 2009
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iUBICOM'11 Proceedings of the 6th international conference on Ubiquitous and Collaborative Computing
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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.