VISUALIZATION OF MULTIVARIATE DATA USING EXPANDED CONSTELLATION AND EXPANDED KANJI GRAPHS AND THEIR APPLICATION TO CLUSTERING

Abstract

In this study, expanded constellation and expanded kanji graphs are proposed and the effectiveness of these graphs in the multivariate analysis is examined. To draw the expanded constellation graph, the variables are first placed on the circumference of the semicircle using factor loadings. Then, a line is drawn inside the semicircle by connecting the vectors of each variable. The constellation graph can simultaneously grasp both the tendencies of the objects and 𝑝 -dimensional variables. In the expanded kanji graph, the height and width of the kanji are determined using the mean values of the variables for each cluster. The kanjis are placed around the radar chart drawn using the standard deviation. The kanji graph can intuitively grasp the characteristics of each group in the cluster analysis. Herein, the effectiveness of the proposed methods is verified by evaluating the flavor of whiskey. The proposed methods show that whiskey flavors can be classified into five clusters: (1) “full-body and winey type,” (2) “sweet and balanced type,” (3) “smoky and balanced type,” (4) “full-body and smoky type,” and (5) “light-body and sweet floral type.”