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Correlation Coordinate Plots: Efficient Layouts for Correlation Tasks

  • Conference paper
  • First Online:
Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 693))

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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.

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Notes

  1. 1.

    CCPs: https://github.com/hoa84/CCPs_SnowflakeViz.

  2. 2.

    http://lib.stat.cmu.edu/datasets/boston.

  3. 3.

    http://lib.stat.cmu.edu/datasets/pollen.data.

  4. 4.

    http://vis.computer.org/vis2004contest/.

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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.

Author information

Authors and Affiliations

  1. Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, USA

    Hoa Nguyen

  2. Department of Computer Science and Engineering, University of South Florida, Tampa, USA

    Paul Rosen

Authors
  1. Hoa Nguyen
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  2. Paul Rosen
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Corresponding author

Correspondence to Hoa Nguyen .

Editor information

Editors and Affiliations

  1. Escola Superior de Tecnologia do IPS, Setúbal, Portugal

    José Braz

  2. MiraLab, University of Geneva, Carouge, Switzerland

    Nadia Magnenat-Thalmann

  3. LISA - ISTIA, University of Angers, Angers, France

    Paul Richard

  4. Department of Computer Science and Electrical Engineering, Jacobs University, Bremen, Germany

    Lars Linsen

  5. University of Groningen, Groningen, The Netherlands

    Alexandru Telea

  6. Università di Catania, Catania, Italy

    Sebastiano Battiato

  7. Research Innovation Center, Canon U.S.A. Inc., San Jose, California, USA

    Francisco Imai

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Nguyen, H., Rosen, P. (2017). Correlation Coordinate Plots: Efficient Layouts for Correlation Tasks. In: Braz, J., et al. Computer Vision, Imaging and Computer Graphics Theory and Applications. VISIGRAPP 2016. Communications in Computer and Information Science, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-64870-5_13

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  • DOI: https://doi.org/10.1007/978-3-319-64870-5_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-64869-9

  • Online ISBN: 978-3-319-64870-5

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