Expression of Vagueness in Factor Space

Abstract

Kansei Engineering and Kansei Information Processing are often used recently to understand the impression or feeling of people for physical objects. A set of qualitative data obtained by rating a product usually has a large variance reflecting tastes and preferences of individuals. It is sensible to express such fluctuations by fuzzy numbers to treat vagueness and uncertainty of the feeling of individuals. This paper proposes a factor analysis technique for fuzzy data of rating scores measured by words that are mainly adjectives. Fuzzy factor loading is determined as fuzzy numbers through a data mapping technique. Thus, words are identified as fuzzy objects in the factor space. After fuzzy distances between words in the factor space are defined, a number of clustering techniques are examined to obtain a partition of words.