Categorizing Quantities using an Interactive Fuzzy Membership Function
Liqun Liu, Romain Vuillemot · 2021
In this paper, we investigate how an interactive version of the membership function from the Fuzzy Logic Theory can be used to categorize quantitative data. This function is simple and similar to a line chart, and provides an explicit mapping of the categorization process. We first review the requirements for such quantitative values partitioning process and provide the Fuzzy Logic mathematical foundations related to the membership function. We then report on the implementation of the interactive function for several quantitative datasets case studies (e. g., age, temperature, speed). We expect this interactive function to provide more control over the categorization process, as well as way to make the categorization more explicit.