Abstract
In this paper, we introduce a fuzzy mining algorithm for discovering generalized association rules with multiple supports of items for extracting implicit knowledge from quantitative transaction data. The proposed algorithm first adopts the fuzzy-set concept to transform quantitative values in transactions into linguistic terms. Besides, each primitive item is given its respective predefined support threshold. The minimum support for an item at a higher taxonomic concept is set as the minimum of the minimum supports of the items belonging to it and the minimum support for an itemset is set as the maximum of the minimum supports of the items contained in the itemset. An example is also given to demonstrate that the proposed mining algorithm can derive the generalized association rules under multiple minimum supports in a simple and effective way.
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Lee, YC., Hong, TP., Chen, CH. (2010). Mining Generalized Association Rules with Quantitative Data under Multiple Support Constraints. In: Pan, JS., Chen, SM., Nguyen, N.T. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2010. Lecture Notes in Computer Science(), vol 6422. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16732-4_24
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DOI: https://doi.org/10.1007/978-3-642-16732-4_24
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