Computer Graphics
TU Braunschweig

Quality-Based Visualization Matrices

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.

Author(s):Georgia Albuquerque, Martin Eisemann, Dirk. J. Lehmann, Holger Theisel, Marcus Magnor
Published:November 2009
Type:Article in conference proceedings
Book:Proc. Vision, Modeling and Visualization (VMV)
Presented at:Vision, Modeling and Visualization (VMV) 2009
Project(s): Scalable Visual Analytics 

  title = {Quality-Based Visualization Matrices},
  author = {Albuquerque, Georgia and Eisemann, Martin and Lehmann, Dirk. J. and Theisel, Holger and Magnor, Marcus},
  booktitle = {Proc. Vision, Modeling and Visualization ({VMV})},
  organization = {Eurographics},
  pages = {341--349},
  month = {Nov},
  year = {2009}