# mutual information vs normalized mutual information

I would like to know why some paper uses Normalized Mutual Information and not standard Mutual Information to measure correlation between features? what is the difference between these two measures?

• Mutual Information I(X,Y) yelds values from $0$ (no mutual information - variables X and Y are independent) to $+\infty$. The higher the I(X,Y), the more information is shared between X and Y. However, high values of mutual information might be unintuitive and hard to interpret due to its unbounded range of values $I(X,Y)\in [0...\infty)$.
• Normalized Mutual Information measures try to bring the possible values to bounded range $I(X,Y)\in [0...m]$. Specifically, case of $m=1$ is useful due to ease of comparison with commonly used correlation coefficients.