Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/PacificVis.2014.43 | PacificVis |
Keywords | Field | DocType |
categorical data visualization,discrete nature,effective visualization,discrete category,great interest,entropy-related measures,categorical variable,multidimensional visualization technique,mutual information,direct application,joint entropy,massive high dimensional categorical,data visualisation,categorical variables,entropy,measurement,data mining,data visualization,visualization | Data mining,Data visualization,Information visualization,Visualization,Categorical variable,Knowledge extraction,Mutual information,Joint entropy,Mathematics,Creative visualization | Conference |
ISSN | Citations | PageRank |
2165-8765 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J Alsakran | 1 | 45 | 4.33 |
| Xiaoke Huang | 2 | 0 | 0.34 |
| Ye Zhao | 3 | 126 | 8.34 |
| Jing Yang | 4 | 163 | 7.35 |
| Karl Fast | 5 | 12 | 1.04 |