Lee, S, Kim, J, Choi, Y and Choi, Y(2024). Classification and discrimination of excel radial charts using the statistical shape analysis, The Korean Journal of Applied Statistics, 27(1), 73-86.

Abstract

A radial chart of Excel is very useful graphical method in delivering information for numerical data. However,

it is not easy to discriminate or classify many individuals. In this case, after shaping each individual of a radial

chart, we need to apply shape analysis.

For a radial chart, since landmarks for shaping are formed as many as the number of variables representing the

characteristics of the x-x-object, we consider a shape that connects them to a line. If the shape becomes complicated

due to the large number of variables, it is di?cult to easily grasp even if visualized using a radial chart. Principal

component analysis (PCA) is performed on variables to create a visually e?ective shape. The classi?cation table

and classi?cation rate are checked by applying the techniques of traditional discriminant analysis, support vector

machine (SVM), and arti?cial neural network (ANN), before and after principal component analysis. In addition,

the di?erence in discrimination between the two coordinates of generalized procrustes analysis (GPA) coordinates

and Bookstein coordinates is compared. Bookstein coordinates are obtained by converting the position, rotation,

and scale of the shape around the base landmarks, and show higher rate than GPA coordinates for the classi?cation

rate.

Keywords: shape analysis, radial char t, GPA, bookstein coordinates