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

Abstract

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 nonstationary 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.

Prediction of O3 Concentration Level Using Fuzzy Non-Stationary Method

“…At this stage, two non-stationary fuzzy parameters are determined, which consist of variations of non-stationary form and perturbation function. Variations in non-stationary forms using location variations can be written using (1) [16].…”