Journal
COMPUTER GRAPHICS FORUM
Volume 32, Issue 8, Pages 158-169Publisher
WILEY
DOI: 10.1111/cgf.12194
Keywords
categorical data; multivariate data; dimensionality reduction; exploratory analysis; I; 3 [Computer Graphics]; Interaction techniques; I; 3; 8 Applications
Categories
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available