Improved Identification of Data Correlations through Correlation Coordinate Plots

Hoa Nguyen, Paul Rosen

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 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.

Paper Citation

in Harvard Style

Nguyen H. and Rosen P. (2016). Improved Identification of Data Correlations through Correlation Coordinate Plots . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: IVAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 60-71. DOI: 10.5220/0005717500600071

in Bibtex Style

@conference{ivapp16,
author={Hoa Nguyen and Paul Rosen},
title={Improved Identification of Data Correlations through Correlation Coordinate Plots},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: IVAPP, (VISIGRAPP 2016)},
year={2016},
pages={60-71},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005717500600071},
isbn={978-989-758-175-5},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2: IVAPP, (VISIGRAPP 2016)
TI - Improved Identification of Data Correlations through Correlation Coordinate Plots
SN - 978-989-758-175-5
AU - Nguyen H.
AU - Rosen P.
PY - 2016
SP - 60
EP - 71
DO - 10.5220/0005717500600071