Abstract
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.
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© 2001 Springer-Verlag Berlin Heidelberg
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Feng, L., Li, Q., Wong, A. (2001). Mining Inter-Transactional Association Rules: Generalization and Empirical Evaluation. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds) Data Warehousing and Knowledge Discovery. DaWaK 2001. Lecture Notes in Computer Science, vol 2114. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44801-2_4
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DOI: https://doi.org/10.1007/3-540-44801-2_4
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