Efficient Mining of Cross-Transaction Web Usage Patterns in Large Database

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 crosstransaction web usage patterns from large database efficiently.