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Correlation Coordinate Plots: Efficient Layouts for Correlation Tasks

  • Hoa NguyenEmail author
  • Paul Rosen
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 693)

Abstract

Correlation is a powerful measure of relationships assisting in estimating trends and making forecasts. It’s use is widespread, being a critical data analysis component of fields including science, engineering, and business. Unfortunately, visualization methods used to identify and estimate correlation are designed to be general, supporting many visualization tasks. Due in large part to their generality, they do not provide the most efficient interface, in terms of speed and accuracy for correlation identifying. To address this shortcoming, we first propose a new correlation task-specific visual design called Correlation Coordinate Plots (CCPs). CCPs transform data into a powerful coordinate system for estimating the direction and strength of correlation. To extend the functionality of this approach to multiple attribute datasets, we propose two approaches. The first design is the Snowflake Visualization, a focus+context layout for exploring all pairwise correlations. The second design enhances the CCP by using principal component analysis to project multiple attributes. We validate CCP by applying it to real-world data sets and test its performance in correlation-specific tasks through an extensive user study that showed improvement in both accuracy and speed of correlation identification.

Keywords

Correlation identification Correlation visualization Multidimensional data visualization 

Notes

Acknowledgements

We would like to thank our reviewers and colleagues who gave us valuable feedback on our approach. We would also like to thank our funding agents, NSF CIF21 DIBBs (ACI-1443046), NSF Core Program (IIS-1513616), Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory Analysis in Motion (AIM) Initiative.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Scientific Computing and Imaging InstituteUniversity of UtahSalt Lake CityUSA
  2. 2.Department of Computer Science and EngineeringUniversity of South FloridaTampaUSA

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