We develop methodology for 3D radial visualization (RadViz) of
high-dimensional datasets. Our display engine is called RadViz3D and extends
the classical 2D RadViz that visualizes multivariate data in the 2D plane by
mapping every record to a point inside the unit circle. We show that
distributing anchor points at least approximately uniformly on the 3D unit
sphere provides a better visualization with minimal artificial visual
correlation for data with uncorrelated variables. Our RadViz3D methodology
therefore places equi-spaced anchor points, one for every feature, exactly for
the five Platonic solids, and approximately via a Fibonacci grid for the other
cases. Our Max-Ratio Projection (MRP) method then utilizes the group
information in high dimensions to provide distinctive lower-dimensional
projections that are then displayed using Radviz3D. Our methodology is extended
to datasets with discrete and continuous features where a Gaussianized
distributional transform is used in conjunction with copula models before
applying MRP and visualizing the result using RadViz3D. A R package radviz3d
implementing our complete methodology is available.Comment: 12 pages, 10 figures, 1 tabl
