Abstract
Recent growth of startup companies in the area of Web Usage Mining is a strong indication of the effectiveness of this data in understanding user behaviors. However, the approach taken by industry towards Web Usage Mining is off-line and hence intrusive, static, and cannot differentiate between various roles a single user might play. Towards this end, several researchers studied probabilistic and distance-based models to summarize the collected data and maintain only the important features for analysis. The proposed models are either not flexible to trade-off accuracy for performance per application requirements, or not adaptable in real-time due to high complexity of updating the model. In this paper, we propose a new model, the FM model, which is flexible, tunable, adaptable, and can be used for both anonymous and online analysis. Also, we introduce a novel similarity measure for accurate comparison among FM models of navigation paths or cluster of paths. We conducted several experiments to evaluate and verify the FM model.
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References
Cadez I., Heckerman D., Meek C, Smyth P., and White S.: Visualization of Navigation Patterns on Web-Site Using Model Based Clustering. Technical Report MSR-TR-00-18, Microsoft Research, Microsoft Corporation, Redmond, WA,(2000)
Huberman B., Pirolli P., Pitkow J., and Lukos R.: Strong Regularities in World Wide Web Surfing. Science, 280, p.p 95–97 (1997)
Mobasher B., Cooley R., and Srivastava J.: Automatic Personalization Based on Web Usage Mining. Special Section of the Communications of ACM on “Personalization Technologies with Data Mining”, 43(8):142–151 (2000)
Mulvenna M.D., Anand S.S., Büchner A.G.: Personalization on the Net using Web mining: Introduction. CACM 43(8): 122–125 (2000)
Perkowitz M., Etzioni O.: Toward Adaptive Web-Sites: Conceptual Framework and Case Study. Artificial Intelligence 118, p.p 245–275 (2000)
Shahabi C., Zarkesh A.M., Adibi J., and Shah V.: Knowledge Discovery from Users Web Page Navigation. IEEE RIDE97 Workshop, April (1997)
Spiliopoulou M.: Web usage mining for site evaluation: Making a site better fit its users. Special Section of the Communications of ACM on “Personalization Technologies with Data Mining”, 43(8):127–134 (2000)
Srivastava J., Cooley r., Deshpande M., Tan M.N.: Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. SIGKDD Explorations, Vol. 1, Issue 2 (2000)
Yan T.W., Jacobsen M., Garcia-Molina H., Dayal U.: From User Access Patterns to Dynamic Hypertext Linking. Fifth International World Wide Web Conference, Paris, France, (1996)
Zarkesh A., Adibi J., Shahabi C., Sadri R., and Shah V.: Analysis and Design of Server Informative WWW-Sites. ACM CIKM’ 97 (1997)
Shahabi C., Banaei-Kashani F., Faruque J., Faisal A.: Feature Matrices: A Model for Efficient and Anonymous Mining of Web Navigations. Technical Report USC-CS-00-736, Computer Science Department, University of Southern California.
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HREF1: http://www.personify.com
HREF2: http://www.websidestory.com
HREF3: http://www.webtrends.com
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© 2001 Springer-Verlag Berlin Heidelberg
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Shahabi, C., Banaei-Kashani, F., Faruque, J., Faisal, A. (2001). Feature Matrices: A Model for Efficient and Anonymous Web Usage Mining. In: Bauknecht, K., Madria, S.K., Pernul, G. (eds) Electronic Commerce and Web Technologies. EC-Web 2001. Lecture Notes in Computer Science, vol 2115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44700-8_27
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DOI: https://doi.org/10.1007/3-540-44700-8_27
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