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Y. Ping Huang and L. Kao, “Using Fuzzy Support and Confidence Setting to Mine Interesting Association Rules,” IEEE Annual meeting, Vol. 2, 2004, pp. 514-519.

has been cited by the following article:

• TITLE: Improvement of Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

  AUTHORS: Alireza Mirzaei Nejad Kousari, Seyed Javad Mirabedini, Ehsan Ghasemkhani

  KEYWORDS: Association Rule; Data Mining; Fuzzy Set; Quantitative Value; Taxonomy

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.5 No.3, March 29, 2012

  ABSTRACT: Data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Extracting multilevel association rules in transaction databases is most commonly used in data mining. This paper proposes a multilevel fuzzy association rule mining model for extraction of implicit knowledge which stored as quantitative values in transactions. For this reason it uses different support value at each level as well as different membership function for each item. By integrating fuzzy-set concepts, data-mining technologies and multiple-level taxonomy, our method finds fuzzy association rules from transaction data sets. This approach adopts a top-down progressively deepening approach to derive large itemsets and also incorporates fuzzy boundaries instead of sharp boundary intervals. Comparing our method with previous ones in simulation shows that the proposed method maintains higher precision, the mined rules are closer to reality, and it gives ability to mine association rules at different levels based on the user’s tendency as well.