Abstract
This paper introduces a novel pattern called indirect association and examines its utility in various application domains. Existing algorithms for mining associations, such as Apriori, will only discover itemsets that have support above a user-defined threshold. Any itemsets with support below the minimum support requirement are filtered out. We believe that an infrequent pair of items can be useful if the items are related indirectly via some other set of items. In this paper, we propose an algorithm for deriving indirectly associated itempairs and demonstrate the potential application of these patterns in the retail, textual and stock market domains.
supported by NSF ACI-9982274 and AHPCRC Contract No. DAAH04-95-C-0008.
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© 2000 Springer-Verlag Berlin Heidelberg
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Tan, PN., Kumar, V., Srivastava1, J. (2000). Indirect Association: Mining Higher Order Dependencies in Data. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_77
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DOI: https://doi.org/10.1007/3-540-45372-5_77
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