Improved Identification Of Data Correlations Through Correlation Coordinate Plots

Improved Identification Of Data Correlations Through Correlation Coordinate Plots
Hoa Nguyen, and Paul Rosen
Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016

Abstract

Correlation is a powerful relationship measure used in science, engineering, and business to estimate trends and make forecasts. Visualization methods, such as scatterplots and parallel coordinates, are designed to be general, supporting many visualization tasks, including identifying correlation. However, due to their generality, they do not provide the most efficient interface, in terms of speed and accuracy. This can be problematic when a task needs to be repeated frequently. To address this shortcoming, we propose a new correlation textit{task-specific} visualization method called Correlation Coordinate Plots (CCPs). CCPs transform data into a powerful coordinate system for estimating the direction and strength of correlation. To support multiple attributes, we propose 2 additional interfaces. The first is the Snowflake Visualization, a focus+context layout for exploring all pairwise correlations. The second enhances the basic CCP by using principal component analysis to project multiple attributes. We validate CCP performance in correlation-specific tasks through an extensive user study that shows improvement in both accuracy and speed.

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Citation

Hoa Nguyen, and Paul Rosen. Improved Identification Of Data Correlations Through Correlation Coordinate Plots. Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 2016.

Bibtex


@inproceedings{nguyen2016improved,
  title = {Improved Identification of Data Correlations through Correlation Coordinate
    Plots},
  author = {Nguyen, Hoa and Rosen, Paul},
  booktitle = {Joint Conference on Computer Vision, Imaging and Computer Graphics Theory
    and Applications},
  series = {IVAPP},
  pages = {60--71},
  year = {2016},
  note = {textit{Best Paper Award}.},
  abstract = {Correlation is a powerful relationship measure used in science,
    engineering, and business to estimate trends and make forecasts. Visualization methods,
    such as scatterplots and parallel coordinates, are designed to be general, supporting
    many visualization tasks, including identifying correlation. However, due to their
    generality, they do not provide the most efficient interface, in terms of speed and
    accuracy. This can be problematic when a task needs to be repeated frequently. To
    address this shortcoming, we propose a new correlation textit{task-specific}
    visualization method called Correlation Coordinate Plots (CCPs). CCPs transform data
    into a powerful coordinate system for estimating the direction and strength of
    correlation. To support multiple attributes, we propose 2 additional interfaces. The
    first is the Snowflake Visualization, a focus+context layout for exploring all pairwise
    correlations. The second enhances the basic CCP by using principal component analysis to
    project multiple attributes. We validate CCP performance in correlation-specific tasks
    through an extensive user study that shows improvement in both accuracy and speed.}
}