18 January 2010 Visual analytics of large multidimensional data using variable binned scatter plots
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Abstract
The scatter plot is a well-known method of visualizing pairs of two-dimensional continuous variables. Multidimensional 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.
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Ming C. Hao, Ming C. Hao, Umeshwar Dayal, Umeshwar Dayal, Ratnesh K. Sharma, Ratnesh K. Sharma, Daniel A. Keim, Daniel A. Keim, Halldór Janetzko, Halldór Janetzko, } "Visual analytics of large multidimensional data using variable binned scatter plots", Proc. SPIE 7530, Visualization and Data Analysis 2010, 753006 (18 January 2010); doi: 10.1117/12.840142; https://doi.org/10.1117/12.840142
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