Mining Inter-Transactional Association Rules: Generalization and Empirical Evaluation
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 intratransactional associations to multidimensional inter-transactional associations. Inter-transactional association rules can represent not only the associations of items happening within transactions as traditional intratransactional 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 experiments on applying the algorithm to real-life data sets. Empirical evaluation shows that with the generalized intertransactional association rules, more comprehensive and interesting association relationships can be detected.
KeywordsAssociation Rule Mining Association Rule Database Transaction Mining Context Quantitative Association Rule
Unable to display preview. Download preview PDF.
- 1.R. Agrawal, T. Imielinski, and A. Swami. Mining association rules between sets of items in large databases. In Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, pages 207–216, 1993.Google Scholar
- 2.R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. of the 20th Intl. Conf. on Very Large Data Bases, pages 478–499, 1994.Google Scholar
- 3.L. Feng, H. Lu, J. Yu, and J. Han. Mining inter-transaction association rules with templates. In Proc. ACM CIKM Intl. Conf. Information and Knowledge Management, pages 225–233, 1999.Google Scholar
- 5.H. Lu, J. Han, and L. Feng. Stock movement prediction and n-dimensional intertransaction association rules. In Proc. of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, pages 12:1–12:7, 1998.Google Scholar
- 6.K.H. Tung, H. Lu, J. Han, and L. Feng. Breaking the barrier of transcations: Mining inter-transaction association rules. In Proc. ACM SIGKDD Intl. Conf. Knowledge Discovery and Data Mining, pages 297–301, 1999.Google Scholar