The Community for Technology Leaders
Visualization Symposium, IEEE Pacific (2014)
Yokohama, Japan Japan
Mar. 4, 2014 to Mar. 7, 2014
pp: 81-88
ABSTRACT
A wide variety of real-world applications generate massive high dimensional categorical datasets. These datasets contain categorical variables whose values comprise a set of discrete categories. Visually exploring these datasets for insights is of great interest and importance. However, their discrete nature often confounds the direct application of existing multidimensional visualization techniques. We use measures of entropy, mutual information, and joint entropy as a means of harnessing this discreteness to generate more effective visualizations. We conduct user studies to assess the benefits in visual knowledge discovery.
INDEX TERMS
Data visualization, Entropy, Joints, Mutual information, Image color analysis, Visualization, Measurement,Parallel Sets, Categorical data visualization, Dimension Management, Entropy, Mutual Information
CITATION
Jamal Alsakran, Xiaoke Huang, Ye Zhao, Jing Yang, Karl Fast, "Using Entropy-Related Measures in Categorical Data Visualization", Visualization Symposium, IEEE Pacific, vol. 00, no. , pp. 81-88, 2014, doi:10.1109/PacificVis.2014.43
209 ms
(Ver 3.3 (11022016))