Abstract :
The properties of a two-dimensional display whose coordinates are the Euclidean distances from two points in a multivariate space are presented. When used in conjunction with three linear normalization procedures, this display is a useful tool in both supervised and unsupervised classification problems. In addition, some geometric structure is preserved by this mapping. Examples using well-known Iris data are presented to demonstrate the display characteristics.
Keywords :
Clustering, dimensionality reduction, display mapping, iterative operation, multivariate data analysis, optimal decision boundaries, pattern recognition, supervised classification, unsupervised classification.; Computer displays; Data analysis; Data structures; Density functional theory; Euclidean distance; Iris; Nonlinear distortion; Pattern recognition; Two dimensional displays; Vectors; Clustering, dimensionality reduction, display mapping, iterative operation, multivariate data analysis, optimal decision boundaries, pattern recognition, supervised classification, unsupervised classification.;
