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Ko, Sungahn
IVADER: Interactive Visual Analysis and Data Exploration Research
Research Interests
  • Information visualization, visual analytics, human-computer interaction (HCI)

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Automated Box-Cox Transformations for Improved Visual Encoding

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Title
Automated Box-Cox Transformations for Improved Visual Encoding
Author
Maciejewski, RossPattath, AvinKo, SungahnHafen, RyanCleveland, WilliamEbert, David
Keywords
Data transformation; color mapping; statistical analysis; Box-Cox; normal distribution
Issue Date
2013-01
Publisher
IEEE COMPUTER SOC
Citation
IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS, v.19, no.1, pp.130 - 140
Abstract
The concept of preconditioning data (utilizing a power transformation as an initial step) for analysis and visualization is well established within the statistical community and is employed as part of statistical modeling and analysis. Such transformations condition the data to various inherent assumptions of statistical inference procedures, as well as making the data more symmetric and easier to visualize and interpret. In this paper, we explore the use of the Box-Cox family of power transformations to semiautomatically adjust visual parameters. We focus on time-series scaling, axis transformations, and color binning for choropleth maps. We illustrate the usage of this transformation through various examples, and discuss the value and some issues in semiautomatically using these transformations for more effective data visualization.
URI
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DOI
10.1109/TVCG.2012.64
ISSN
1077-2626
Appears in Collections:
ECE_Journal Papers
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