From Intra-transaction to Generalized Inter-transaction: Landscaping Multidimensional Contexts in Association Rule Mining

Q Li, L. Feng, A.K.Y. Wong

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22 Citations (Scopus)
2 Downloads (Pure)


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.
Original languageUndefined
Article number10.1016/j.ins.2004.07.006
Pages (from-to)361-395
Number of pages35
JournalInformation sciences
Issue number3--4
Publication statusPublished - Jun 2005


  • IR-63242
  • METIS-229574
  • Multidimensional context
  • Point/scope-wise
  • Intra-transactional association rule
  • EWI-6312
  • Generalized inter-transactional association rule

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