Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/39936
Title: Discovering web usage patterns by mining cross-transaction association rules
Authors: Chen, J.
Yin, J.
Tung, A.K.H. 
Liu, B.
Keywords: Association rules
Cross-transaction
Frequent closed pageviews sets
Usage patterns
Web usage mining
Issue Date: 2004
Source: Chen, J.,Yin, J.,Tung, A.K.H.,Liu, B. (2004). Discovering web usage patterns by mining cross-transaction association rules. Proceedings of 2004 International Conference on Machine Learning and Cybernetics 5 : 2655-2660. ScholarBank@NUS Repository.
Abstract: Web Usage Mining is the application of data mining techniques to large Web log databases in order to extract usage patterns. However, most of the previous studies on usage patterns discovery just focus on mining intra -transaction associations, i.e., the associations among items within the same user transaction. A cross-transaction association rule describes the association relationships among different user transactions. In this paper, the closure property of frequent itemsets is used to mining cross-transaction association rules from web log databases. An approach and algorithmic framework beads on it is designed and analyzed.
Source Title: Proceedings of 2004 International Conference on Machine Learning and Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/39936
ISBN: 0780384032
Appears in Collections:Staff Publications

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