# What is the concept of Normalized Mutual Information in the evaluation of Clustering?

I know what mutual information basically is but not quite sure about why and how it is used in the context of evaluation of clustering mechanisms ? Can someone please explain the intuition behind it ? i.e, how it is defined in the case of clustering evaluation ?

Then comes NMI which is bias-corrected for the phenomenon explained above and also normalizes the score between $$0$$ and $$1$$ (MI does not have an upper bound).