Relational Clustering Based on a New Robust Estimator with Application to Web Mining
Links to Fileshttps://ieeexplore.ieee.org/document/781785
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Type of Work5 pages
conference papers and proceedings preprints
Citation of Original PublicationOlfa Nasraoui, Raghu Krishnapuram, and Anupam Joshi, Relational Clustering Based on a New Robust Estimator with Application to Web Mining, 18th International Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.99TH8397), 1999, DOI: 10.1109/NAFIPS.1999.781785
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© 1999 IEEE
uniform resource locators
fuzzy set theory
UMBC Ebiquity Research Group
Mining typical user profiles and URL associations from the vast amount of access logs is an important component of Web personalization. In this paper, we define the notion of a ""user session" as being a temporally compact sequence of Web accesses by a user. We also define a dissimilarity measure between two Web sessions that captures the organization of a Web site. To cluster the user sessions based on the pairwise dissimilarities, we introduce the relational fuzzy c-maximal density estimator (RFC-MDE) algorithm. RFC-MDE is robust and can deal with outliers that are typical in this application. We show real examples of the use of RFC-MDE for extraction of user profiles from log data, and and compare its performance to the standard non-Euclidean fuzzy c-means.