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 |
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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 | |
Publication status | Published - 2013 |