I have to cluster some data using non-parametric clustering technique which is given in this paper. After all the cluster evaluation measure used in this paper is Normalized Mutual Information as they people know about groupings before hand.
In my case, the data which I have used is not labelled empirically, though I've to cluster it using same technique which I've accomplished almost. So far what I know is, we use to use internal evaluation measures (such as Davies-Bouldin Index, Dunn Index, CD Index or Silhouette Index etc.) when there is no ground truth and external evaluation measures (such as Purity, Precision, Recall, F-Measure or NMI etc.) when there exist some ground truth to match. But as here in my case there is no proper ground truth to match.
Which specific evaluation measure (say internal measure) will I choose from internal measures set (if I'm right to choose from this set) to evaluate clustering results?
I would like to learn how to choose clustering evaluation measure (i.e. internal or external) according to context.