Dunn's index

This is an implementation of the Dunn's index for clustering.

The Dunn's index measures compactness (Maximum distance in between data points of clusters) and clusters separation (minimum distance between clusters). This measurement serves as a measure to find the right number of clusters in a data set, where the maximum value of the index represents the right partitioning given the index (partition with the highest separation between clusters and less spread data in between clusters).

For more information about the Dunn's index check:

Validity index for crisp and fuzzy clusters, Malay K. Pakhira, Pattern Recognition Volume 37, Issue 3, March 2004, Pages 487-501