Visual Analysis of Multi-Dimensional Categorical Data Sets
Article first published online: 21 OCT 2013
© 2013 The Authors Computer Graphics Forum © 2013 The Eurographics Association and John Wiley & Sons Ltd.
Computer Graphics Forum
Volume 32, Issue 8, pages 158–169, December 2013
How to Cite
Broeksema, B., Telea, A. C. and Baudel, T. (2013), Visual Analysis of Multi-Dimensional Categorical Data Sets. Computer Graphics Forum, 32: 158–169. doi: 10.1111/cgf.12194
- Issue published online: 27 NOV 2013
- Article first published online: 21 OCT 2013
- categorical data;
- multivariate data;
- dimensionality reduction;
- exploratory analysis;
- I.3 [Computer Graphics]:; Interaction techniques;
- I.3.8 Applications
We present a set of interactive techniques for the visual analysis of multi-dimensional categorical data. Our approach is based on multiple correspondence analysis (MCA), which allows one to analyse relationships, patterns, trends and outliers among dependent categorical variables. We use MCA as a dimensionality reduction technique to project both observations and their attributes in the same 2D space. We use a treeview to show attributes and their domains, a histogram of their representativity in the data set and as a compact overview of attribute-related facts. A second view shows both attributes and observations. We use a Voronoi diagram whose cells can be interactively merged to discover salient attributes, cluster values and bin categories. Bar chart legends help assigning meaning to the 2D view axes and 2D point clusters. We illustrate our techniques with real-world application data.