Mining Unexpected Web Usage Behaviors
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Web 2.0 proxy: upgrading websites from web 1.0 to web 2.0
WSEAS TRANSACTIONS on COMMUNICATIONS
Mining convergent and divergent sequences in multidimensional data
International Journal of Business Intelligence and Data Mining
Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach
Journal of Intelligent Information Systems
WebUser: mining unexpected web usage
International Journal of Business Intelligence and Data Mining
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Existing Web usage mining techniques are currently based on an arbitrary division of the data (e.g. "one log per month") or guided by presumed results (e.g. "what is the customers' behaviour for the period of Christmas purchases?"). These approaches have two main drawbacks. First, they depend on the above-mentioned arbitrary organization of data. Second, they cannot automatically extract "seasonal peaks" from among the stored data. In this paper, we propose a specific data mining process (in particular, to extract frequent behaviour patterns) in order to reveal the densest periods automatically. From the whole set of possible combinations, our method extracts the frequent sequential patterns related to the extracted periods. A period is considered to be dense if it contains at least one frequent sequential pattern for the set of users connected to the website in that period. Our experiments show that the extracted periods are relevant and our approach is able to extract both frequent sequential patterns and the associated dense periods.