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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.

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  • $\begingroup$ When I was programming some popular internal clustering criterions I described, superficially their properties - as I perceived it - in a tech document (find it on my web-page, "Clustering criterions", most important parts are in english). $\endgroup$
    – ttnphns
    Jun 25, 2016 at 21:41
  • $\begingroup$ Your choice will depend on the nature of the data (continuous - hence distance-based clusters, or categorical, hence more count-based clusters); on the shape of clusters (are they gaussian-like or, say, worm-like); etc. A thread with a number of further links in comments and answers, about cluster validation. $\endgroup$
    – ttnphns
    Jun 25, 2016 at 21:46
  • $\begingroup$ @ttnphns and my nature of the data is not continuous and shape of clusters most probably be Gaussian-like as I used Dirichlet priors in clustering, so what measure you suggest? $\endgroup$
    – maliks
    Jun 25, 2016 at 23:03
  • $\begingroup$ @ttnphns you haven't provided the URL for you web page "Clustering Criterions" $\endgroup$
    – maliks
    Jun 25, 2016 at 23:12
  • $\begingroup$ @ttnphns would you like to share the url link? $\endgroup$
    – maliks
    Jun 26, 2016 at 9:55

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