scholarly journals Matrix-based visual correlation analysis on large timeseries data

2012 ◽  
Author(s):  
Michael Behrisch ◽  
James Davey ◽  
Tobias Schreck ◽  
Daniel Keim ◽  
Jorn Kohlhammer
Keyword(s):  
Correlation Analysis ◽  
Visual Correlation

scholarly journals Visual Correlation Analysis of Numerical and Categorical Data on the Correlation Map

2015 ◽  
Vol 21 (2) ◽  
pp. 289-303 ◽  
Author(s):  
Zhiyuan Zhang ◽  
Kevin T. McDonnell ◽  
Erez Zadok ◽  
Klaus Mueller
Keyword(s):  
Correlation Analysis ◽  
Categorical Data ◽  
Visual Correlation

Gaze-tracking-based audio-visual correlation analysis employing quality of experience methodology

2010 ◽  
Vol 4 (3) ◽  
pp. 217-227 ◽  
Author(s):  
Bartosz Kunka ◽  
Bozena Kostek ◽  
Maciej Kulesza ◽  
Piotr Szczuko ◽  
Andrzej Czyzewski
Keyword(s):  
Correlation Analysis ◽  
Quality Of Experience ◽  
Gaze Tracking ◽  
Visual Correlation

Judging Correlation from Scatterplots and Parallel Coordinate Plots

2008 ◽  
Vol 9 (1) ◽  
pp. 13-30 ◽  
Author(s):  
Jing Li ◽  
Jean-Bernard Martens ◽  
Jarke J van Wijk
Keyword(s):  
Correlation Analysis ◽  
Statistical Model ◽  
Statistical Test ◽  
Observation Time ◽  
Visualization Method ◽  
Performance Accuracy ◽  
Sample Correlation ◽  
Judgment Process ◽  
Visual Correlation ◽  
User Experiment

Scatterplots and parallel coordinate plots (PCPs) that can both be used to assess correlation visually. In this paper, we compare these two visualization methods in a controlled user experiment. More specifically, 25 participants were asked to report observed correlation as a function of the sample correlation under varying conditions of visualization method, sample size and observation time. A statistical model is proposed to describe the correlation judgment process. The accuracy and the bias in the judgments in different conditions are established by interpreting the parameters in this model. A discriminability index is proposed to characterize the performance accuracy in each experimental condition. Moreover, a statistical test is applied to derive whether or not the human sensation scale differs from a theoretically optimal (that is, unbiased) judgment scale. Based on these analyses, we conclude that users can reliably distinguish twice as many different correlation levels when using scatterplots as when using PCPs. We also find that there is a bias towards reporting negative correlations when using PCPs. Therefore, we conclude that scatterplots are more effective than parallel plots in supporting visual correlation analysis.

Improved visual correlation analysis for multidimensional data

2017 ◽  
Vol 41 ◽  
pp. 121-132 ◽  
Author(s):  
Yi Zhang ◽  
Teng Liu ◽  
Kefei Li ◽  
Jiawan Zhang
Keyword(s):  
Correlation Analysis ◽  
Multidimensional Data ◽  
Visual Correlation
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