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

Simon Coupland; Stephen G. Matthews

doi:10.1109/T2FZZ.2013.6613293

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

Simon Coupland, Stephen G. Matthews

Department of Engineering Mathematics

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

1 Citation (Scopus)

398 Downloads (Pure)

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 language	English
Title of host publication	Proceedings of The 2013 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems (T2FUZZ 2013)
Pages	9-14
Number of pages	6
DOIs	https://doi.org/10.1109/T2FZZ.2013.6613293
Publication status	Published - 2013

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Using-Nonstationary-Fuzzy-Sets-to-Improve-the-Tractability-of-Fuzzy-Association-Rules
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Using nonstationary fuzzy sets to improve the tractability of fuzzy association rules

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