Extension of Star Coordinates into Three Dimensions

Nathan Cooprider Robert Burton
coop@cs.utah.edu rpburton@cs.byu.edu

University of Utah, School of Computing
50 South Central Campus Drive, Room 3190
Salt Lake City, Utah 84112-9205
      Brigham Young University, Computer Science Department
3361 TMCB PO Box 26576
Provo, UT 84602-6576

In Proceedings of Conference on Visualization and Data Analysis (VDA), San Jose, California, January 2007.

Copyright 2007 Society of Photo-Optical Instrumentation Engineers. This paper was be published in VDA 2007 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.


Traditional Star Coordinates displays a multi-variate data set by mapping it to two Cartesian dimensions. This technique facilitates cluster discovery and multi-variate analysis, but binding to two dimensions hides features of the data. Threedimensional Star Coordinates spreads out data elements to reveal features. This allows the user more intuitive freedom to explore and process the data sets.

Three-dimensional Star Coordinates is implemented by extending the data structures and transformation facilities of traditional Star Coordinates. We have given high priority to maintaining the simple, traditional interface. We simultaneously extend existing features, such as scaling of axes, and add new features, such as system rotation in three dimensions. These extensions and additions enhance data visualization and cluster discovery.

We use three examples to demonstrate the advantage of three-dimensional Star Coordinates over the traditional system. First, in an analysis of customer churn data, system rotation in three dimensions gives the user new insight into the data. Second, in cluster discovery of car data, the additional dimension allows the true shape of the data to be seen more easily. Third, in a multi-variate analysis of cities, the perception of depth increases the degree to which multi-variate analysis can occur.

Nathan Cooprider <coop@cs.utah.edu>