Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9651016 | Information Sciences | 2005 | 35 Pages |
Abstract
The problem of mining multidimensional inter-transactional association rules was recently introduced in [ACM Trans. Inform. Syst. 18(4) (2000) 423; Proc. of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Seattle, Washington, June 1998, p. 12:1]. 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 expansion, and present a more general form of association rules, named generalized multidimensional inter-transactional association rules. An algorithm for mining such generalized inter-transactional association rules is presented by extension of a priori. We report our experiments on applying the algorithm to both real-life and synthetic data sets. Empirical evaluation shows that with the generalized inter-transactional association rules, more comprehensive and interesting association relationships can be detected from data sets.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Qing Li, Ling Feng, Allan Wong,