Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Breaking the barrier of transactions: mining inter-transaction association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining inter-transaction associations with templates
Proceedings of the eighth international conference on Information and knowledge management
ACM Transactions on Information Systems (TOIS)
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A tree structure for event-based sequence mining
Knowledge-Based Systems
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The problem of mining multidimensional inter-transactional association rules was recently introduced in [5, 4]. It extends the scope of mining association rules from traditional single-dimensional intra-transactional associations to multidimensional inter-transactional associations. Inter-transactional association rules can represent not only the associations of items happening within transactions as traditional intra-transactional association rules do, but also the associations of items among different transactions under a multidimensional context. "After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away" is an example of such rules. In this paper, we extend the previous problem definition based on context expansions, and present a generalized multidimensional inter-transactional association rule framework. An algorithm for mining such generalized inter-transactional association rules is presented by extension of Apriori. We report our e xperiments on applying the algorithm to real-life data sets. Empirical evaluation shows that with the generalized inter-transactional association rules, more comprehensive and interesting association relationships can be detected.