Abstract
Mining generalized association rules in the presence of the taxonomy has been recognized as an important model in data mining. Earlier work on generalized association rules confined the minimum support to be uniformly specified for all items or for items within the same taxonomy level. In this paper, we extended the scope of mining generalized association rules in the presence of taxonomy to allow any form of user-specified multiple minimum supports. We discussed the problems of using classic Apriori itemset generation and presented two algorithms, MMS Cumulate and MMS Stratify, for discovering the generalized frequent itemsets. Empirical evaluation showed that these two algorithms are very effective and have good linear scale-up characteristic....
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© 2001 Springer-Verlag Berlin Heidelberg
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Tseng, MC., Lin, WY. (2001). Mining Generalized Association Rules with Multiple Minimum Supports. 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_2
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DOI: https://doi.org/10.1007/3-540-44801-2_2
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