Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
COMPSAC '00 24th International Computer Software and Applications Conference
Mining Frequent Purchase Behavior Patterns for Commercial Websites
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
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In this paper, we explore a new data mining capability which involves mining Web transaction patterns for an electronic commerce (EC) environment. We propose an innovative mining model that takes both the traveling patterns and purchasing patterns of customers into consideration. First, we develop algorithm WR to extract meaningful Web transaction records from Web transactions so as to filter out the effect of irrelevant traversal sequences. Second, we devise algorithm WTM for determining the large transaction patterns from the Web transaction records obtained.