Mining Fuzzy Association Rules in Quantitative Databases

Yi Ming Bai; Xian Yao Meng; Xin Jie Han

doi:10.4028/www.scientific.net/AMM.182-183.2003

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 182-183Mining Fuzzy Association Rules in Quantitative...

Mining Fuzzy Association Rules in Quantitative Databases

Abstract:

In this paper, we introduce a novel technique for mining fuzzy association rules in quantitative databases. Unlike other data mining techniques who can only discover association rules in discrete values, the algorithm reveals the relationships among different quantitative values by traversing through the partition grids and produces the corresponding Fuzzy Association Rules. Fuzzy Association Rules employs linguistic terms to represent the revealed regularities and exceptions in quantitative databases. After the fuzzy rule base is built, we utilize the definition of Support Degree in data mining to reduce the rule number and save the useful rules. Throughout this paper, we will use a set of real data from a wine database to demonstrate the ideas and test the models.

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Applied Mechanics and Materials (Volumes 182-183)

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2003-2007

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https://doi.org/10.4028/www.scientific.net/AMM.182-183.2003

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Online since:

June 2012

Authors:

Yi Ming Bai, Xian Yao Meng, Xin Jie Han

Keywords:

Association Rule, Data Mining (DM), Fuzzy Set, Support Degree

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