High-Dimensional Visual Analytics: Interactive Exploration Guided by Pairwise Views of Point Distributions

Description: 

We introduce a method for organizing multivariate displays and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional Euclidean space. These characterizations include such measures as density, skewness, shape, outliers, and texture. Statistical analysis of these measures leads to ways for 1) organizing 2D scatterplots of points for coherent viewing, 2) locating unusual (outlying) marginal 2D distributions of points for anomaly detection, and 3) sorting multivariate displays based on high-dimensional data, such as trees, parallel coordinates, and glyphs.

Authors: 
Leland Wilkinson
Anushka Anand
Robert Grossman
Publication Date: 
Sunday, October 15, 2006
Publication Information: 
Leland Wilkinson, Anushka Anand, Robert Grossman. High-Dimensional Visual Analytics: Interactive Exploration Guided by Pairwise Views of Point Distributions. IEEE Transactions on Visualization and Computer Graphics 12(6), p. 1363-1372 (2006)