Data MiningPredictive Analytics

Visualizing correlation matrices

DavidMSmith
2 Min Read

Looking at the components of a correlation matrix can be an important diagnostic tool.  Simply looking at a table of numbers isn’t practical for a matrix larger than say, 4×4, so graphical visualization tools come into play. Various methods have been proposed in the past, from heat maps to correlation ellipses. Taiyun Wei offers an alternative: correlation circles.  Positive correlations are illustrated with a filled black circle; negative ones with a white circle outlined in black:

 
I think this could be a promising approach when there’s a natural ordering amongst the variables in the matrix, as it does pick up patterns in the correlations well, highlighting the distinction between positive and negative correlations. On the other hand, I think it downplays the negative correlations too much — perhaps a filled gray circle rather than the white outlined circle would alleviate that problem.

Now, I think a game of Othello is in order…

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Looking at the components of a correlation matrix can be an important diagnostic tool.  Simply looking at a table of numbers isn’t practical for a matrix larger than say, 4×4, so graphical visualization tools come into play. Various methods have been proposed in the past, from heat maps to correlation ellipses. Taiyun Wei offers an alternative: correlation circles.  Positive correlations are illustrated with a filled black circle; negative ones with a white circle outlined in black:

 
I think this could be a promising approach when there’s a natural ordering amongst the variables in the matrix, as it does pick up patterns in the correlations well, highlighting the distinction between positive and negative correlations. On the other hand, I think it downplays the negative correlations too much — perhaps a filled gray circle rather than the white outlined circle would alleviate that problem.

Now, I think a game of Othello is in order…

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