Editing Cluster analysis (section)

==== [[Dunn index]] ====
The Dunn index aims to identify dense and well-separated clusters. It is defined as the ratio between the minimal inter-cluster distance to maximal intra-cluster distance. For each cluster partition, the Dunn index can be calculated by the following formula:<ref>{{Cite journal
 | last = Dunn | first = J.
 | title = Well separated clusters and optimal fuzzy partitions
 | journal = Journal of Cybernetics
 | year = 1974
 | volume = 4
 | pages = 95–104
 | doi = 10.1080/01969727408546059
}}</ref>
:<math>
D = \frac{\min_{1 \leq i < j \leq n} d(i,j)}{\max_{1 \leq k \leq n} d^{\prime}(k)} \,,
</math>
where ''d''(''i'',''j'') represents the distance between clusters ''i'' and ''j'', and ''d'' '(''k'') measures the intra-cluster distance of cluster ''k''. The inter-cluster distance ''d''(''i'',''j'') between two clusters may be any number of distance measures, such as the distance between the [[centroids]] of the clusters. Similarly, the intra-cluster distance ''d'' '(''k'') may be measured in a variety of ways, such as the maximal distance between any pair of elements in cluster&nbsp;''k''. Since internal criterion seek clusters with high intra-cluster similarity and low inter-cluster similarity, algorithms that produce clusters with high Dunn index are more desirable.