Interval type-2 fuzzy sets to model linguistic label perception in online services satisfaction

In this paper, we propose a novel two-phase methodology based on interval type-2 fuzzy sets (T2FSs) to model the human perceptions of the linguistic terms used to describe the online services satisfaction. In the first phase, a type-1 fuzzy set (T1FS) model of an individual's perception of the terms used in rating user satisfaction is derived through a decomposition-based procedure. The analysis is carried out by using well-established metrics and results from the Social Sciences context. In the second phase, interval T2FS models of online user satisfaction are calculated using a similarity-based data mining procedure. The procedure selects an essential and informative subset of the initial T1FSs that is used to discard the outliers automatically. Resulting interval T2FSs, which are synthesized based on the selected subset of T1FSs only, exhibit reasonable shapes and interpretability. © 2014 Springer-Verlag Berlin Heidelberg.