Quality-Based Visualization Matrices

Parallel coordinates and scatterplot matrices are
widely used to visualize multi-dimensional data
sets. But these visualization techniques are insufficient
when the number of dimensions grows.
To solve this problem, different approaches to preselect
the best views or dimensions have been proposed
in the last years. However, there are still several
shortcomings to these methods. In this paper
we present three new methods to explore multivariate
data sets: a parallel coordinates matrix, in analogy
to the well-known scatterplot matrix, a classbased
scatterplot matrix that aims at finding good
projections for each class pair, and an importance
aware algorithm to sort the dimensions of scatterplot
and parallel coordinates matrices.