Efficient mining of cross-transaction web usage patterns in large database

Authors:
Jian Chen;Liangyi Ou;Jian Yin;Jin Huang
Affiliations:
Department of Computer Science, Zhongshan University, Guangzhou, China;Department of Computer Science, Zhongshan University, Guangzhou, China;Department of Computer Science, Zhongshan University, Guangzhou, China;Department of Computer Science, Zhongshan University, Guangzhou, China
Venue:
ICCNMC'05 Proceedings of the Third international conference on Networking and Mobile Computing
Year:
2005

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Abstract

Web Usage Mining is the application of data mining techniques to large Web log databases in order to extract usage patterns. A cross-transaction association rule describes the association relationships among different user transactions in Web logs. In this paper, a Linear time intra-transaction frequent itemsets mining algorithm and the closure property of frequent itemsets are used to mining cross-transaction association rules from web log databases. We give the related preliminaries and present an efficient algorithm for efficient mining frequent cross-transaction closed pageviews sets in large Web log database. An extensive performance study shows that our algorithm can mining cross-transaction web usage patterns from large database efficiently.