Name
Abstract | ||
|---|---|---|
The scatter plot is a well-known method of visualizing pairs of two-dimensional continuous variables. Multi-dimensional data can be depicted in a scatter plot matrix. They are intuitive and easy-to-use, but often have a high degree of overlap which may occlude a significant portion of data. In this paper, we propose variable binned scatter plots to allow the visualization of large amounts of data without overlapping. The basic idea is to use a non-uniform (variable) binning of the x and y dimensions and plots all the data points that fall within each bin into corresponding squares. Further, we map a third attribute to color for visualizing clusters. Analysts are able to interact with individual data points for record level information. We have applied these techniques to solve real-world problems on credit card fraud and data center energy consumption to visualize their data distribution and cause-effect among multiple attributes. A comparison of our methods with two recent well-known variants of scatter plots is included. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1117/12.840142 | VISUALIZATION AND DATA ANALYSIS 2010 |
Keywords | Field | DocType |
Variable Binned, Scatter plots, Correlations, Clusters, Cause-Effect, Data Distribution | Data mining,Bin,Computer science,Visual analytics,Artificial intelligence,Cluster analysis,Data point,Computer vision,Bivariate data,Pattern recognition,Visualization,Statistical graphics,Scatter plot | Conference |
Volume | ISSN | Citations |
7530 | 0277-786X | 3 |
PageRank | References | Authors |
0.44 | 4 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming C. Hao | 1 | 3 | 0.44 |
| Umeshwar Dayal | 2 | 8452 | 2538.92 |
| Ratnesh K. Sharma | 3 | 483 | 53.37 |
| Daniel A. Keim | 4 | 7704 | 1141.60 |
| Halldor Janetzko | 5 | 312 | 20.69 |