Methodological Solutions for the Analysis of Imprecise Data: Fuzzy Logic and R-Shiny

Abstract

Imprecise data analysis is a common challenge in various research fields and practical applications. Imprecise observations can arise due to limitations in measurement tools and the inherent nature of the phenomena under study. Fuzzy logic offers a flexible and realistic framework to handle this imprecision. This paper explores the use of fuzzy logic in multivariate analysis of imprecise data, leveraging R Shiny applications to enhance the quality and reliability of the analyses. The proposed methodology includes data coding and standardization, the definition of fuzzy linguistic variables, and the use of multivariate methods for the analysis of fuzzy data.