I am trying to evaluate clustering performance. I was reading the skiscit-learn documentation on metrics. I do not understand the difference between ARI and AMI. It seems to me that they do the same thing in two different ways.
Citing from the documentation:
Given the knowledge of the ground truth class assignments labels_true and our clustering algorithm assignments of the same samples labels_pred, the adjusted Rand index is a function that measures the similarity of the two assignments, ignoring permutations and with chance normalization.
vs
Given the knowledge of the ground truth class assignments labels_true and our clustering algorithm assignments of the same samples labels_pred, the Mutual Information is a function that measures the agreement of the two assignments, ignoring permutations ... AMI was proposed more recently and is normalized against chance.
Should I use both of them in my clustering evaluation or would this be redundant?