Discovering web usage patterns by mining cross-transaction association rules

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.