Mining Inter-Transactional Association Rules: Generalization and Empirical Evaluation

  • Ling Feng
  • Qing Li
  • Allan Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2114)


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.


Association Rule Mining Association Rule Database Transaction Mining Context Quantitative Association Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ling Feng
    • 1
  • Qing Li
    • 2
  • Allan Wong
    • 3
  1. 1.InfoLabTilburgNetherlands
  2. 2.Dept. of Computer ScienceCity University of Hong KongChina
  3. 3.Dept. of ComputingHong Kong Polytechnic UniversityChina

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