Using nonstationary fuzzy sets to improve the tractability of fuzzy association rules

Simon Coupland, Stephen G. Matthews

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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Abstract

Modern organisations now collect very large volumes of data about customers, suppliers and other factors which may impact upon their business. There is a clear need to be able to mine this data and present it to decision makers in a clear and coherent manner. Fuzzy association rules are a popular method to identifying important and meaningful relationships within large data sets. Recently a fuzzy association rule has been proposed that uses the 2-tuple linguistic representation. This paper presents a methodology which makes use of non-stationary fuzzy sets to post process 2-tuple fuzzy association rules reducing the size of the mined rule set by around 20% whilst retaining the semantic meaning of the rule set.
Original languageEnglish
Title of host publicationProceedings of The 2013 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ 2013)
Pages9-14
Number of pages6
DOIs
Publication statusPublished - 2013

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