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Proceedings Paper

Visual analytics of large multidimensional data using variable binned scatter plots
Author(s): Ming C. Hao; Umeshwar Dayal; Ratnesh K. Sharma; Daniel A. Keim; Halldór Janetzko
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Paper 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.

Paper Details

Date Published: 18 January 2010
PDF: 11 pages
Proc. SPIE 7530, Visualization and Data Analysis 2010, 753006 (18 January 2010); doi: 10.1117/12.840142
Show Author Affiliations
Ming C. Hao, Hewlett-Packard Labs. (United States)
Umeshwar Dayal, Hewlett-Packard Labs. (United States)
Ratnesh K. Sharma, Hewlett-Packard Labs. (United States)
Daniel A. Keim, Univ. Konstanz (Germany)
Halldór Janetzko, Univ. Konstanz (Germany)


Published in SPIE Proceedings Vol. 7530:
Visualization and Data Analysis 2010
Jinah Park; Ming C. Hao; Pak Chung Wong; Chaomei Chen, Editor(s)

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