Improved Identification of Data Correlations through Correlation Coordinate Plots
Document Type
Conference Proceeding
Publication Date
2016
Keywords
Correlation, Correlation Visualization, Statistical Visualization
Digital Object Identifier (DOI)
https://doi.org/10.5220/0005717500600071
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
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - IVAPP, 60-71, 2016 , Rome, Italy
Scholar Commons Citation
Nguyen, Hoa and Rosen, Paul, "Improved Identification of Data Correlations through Correlation Coordinate Plots" (2016). Computer Science and Engineering Faculty Publications. 134.
https://digitalcommons.usf.edu/esb_facpub/134
