Name
Abstract | ||
|---|---|---|
The scatter plot is a well-known method of visualizing pairs of two continuous variables. Scatter plots are intuitive and easy-to-use, but often have a high degree of overlap which may occlude a significant portion of the data. To analyze a dense non-uniform data set, a recursive drill-down is required for detailed analysis. In this article, 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 to plot all data points that are located within each bin into the corresponding squares. In the visualization, each data point is then represented by a small cell (pixel). Users are able to interact with individual data points for record level information. To analyze an interesting area of the scatter plot, the variable binned scatter plots with a refined scale for the subarea can be generated recursively as needed. Furthermore, we map a third attribute to color to obtain a visual clustering. We have applied variable binned scatter plots to solve real-world problems in the areas of credit card fraud and data center energy consumption to visualize their data distributions and cause-effect relationships among multiple attributes. A comparison of our methods with two recent scatter plot variants is included. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1057/ivs.2010.4 | Information Visualization |
Keywords | Field | DocType |
basic idea,scatter plot,recent scatter plot variant,continuous variable,data point,individual data point,variable binned scatter plot,dense non-uniform data,data center energy consumption,data distribution,data center,patterns | Data mining,Partial residual plot,Computer science,Artificial intelligence,Cluster analysis,Data point,Bivariate data,Computer vision,Visualization,Algorithm,Pixel,Statistical graphics,Scatter plot | Journal |
Volume | Issue | ISSN |
9 | 3 | 1473-8716 |
Citations | PageRank | References |
6 | 0.61 | 5 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ming C. Hao | 1 | 6 | 0.61 |
| 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 |