Computer Graphics
TU Braunschweig

Selecting Coherent and Relevant Plots in Large Scatterplot Matrices

Selecting Coherent and Relevant Plots in Large Scatterplot Matrices

The scatterplot matrix (SPLOM) is a well-established technique to visually explore high-dimensional data sets. It is characterized by the number of scatterplots (plots) of which it consists of. Unfortunately, this number quadratically grows with the number of the data set’s dimensions. Thus, an SPLOM scales very poorly. Consequently, the usefulness of SPLOMs is restricted to a small number of dimensions. For this, several approaches already exist to explore such ‘small’ SPLOMs. Those approaches address the scalability problem just indirectly and without solving it. Therefore, we introduce a new greedy approach to manage ‘large’ SPLOMs with more than 100 dimensions. We establish a combined visualization and interaction scheme that produces intuitively interpretable SPLOMs by combining known quality measures, a pre-process reordering and a perception-based abstraction. With this scheme, the user can interactively find large amounts of relevant plots in large SPLOMs.

Author(s):Dirk. J. Lehmann, Georgia Albuquerque, Martin Eisemann, Marcus Magnor, Holger Theisel
Published:April 2012
Journal:Computer Graphics Forum Vol. 31
Project(s): Scalable Visual Analytics 

  title = {Selecting Coherent and Relevant Plots in Large Scatterplot Matrices},
  author = {Lehmann, Dirk. J. and Albuquerque, Georgia and Eisemann, Martin and Magnor, Marcus and Theisel, Holger},
  journal = {Computer Graphics Forum},
  volume = {31},
  number = {6},
  pages = {1895--1908},
  month = {Apr},
  year = {2012}