I have labeled data set (with only 2 classes) and I'm trying different clustering algorithms with different variations of similarity measures (which creates different distance matrixes that I give as input to the different clustering algorithms).
In order to select the best pair of clustering algorithm and similarity function I'm comparing the labels with the clustering results. I read about Adjusted rank index, Normalized mutual information and Adjusted mutual information. ARI make sense to me, however, I'm not sure what actually the two others are doing.
- Can you please explain the motivation behind the three? What are the differences?
- Which one is recommended to use in which scenario?