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

Author

Coupland, Simon ; Matthews, Stephen G.

Author_Institution

Centre for Comput. Intell., De Montfort Univ., Leicester, UK

fYear

2013

fDate

16-19 April 2013

Firstpage

9

Lastpage

14

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.

Keywords

computational linguistics; data mining; fuzzy set theory; 2-tuple fuzzy association rules; 2-tuple linguistic representation; fuzzy association rules tractability; nonstationary fuzzy sets; rule set mining; semantic meaning; Association rules; Fuzzy logic; Fuzzy sets; Genetic algorithms; Pragmatics; Web pages;

fLanguage

English

Publisher

ieee

Conference_Titel

Advances in Type-2 Fuzzy Logic Systems (T2FUZZ), 2013 IEEE Symposium on

Conference_Location

Singapore

Type

conf

DOI

10.1109/T2FZZ.2013.6613293

Filename

6613293