FindClusters

FindClusters[{e₁, e₂, ...}]
partitions the

into clusters of similar elements.

FindClusters[{e₁->v₁, e₂->v₂, ...}]
returns the

corresponding to the

in each cluster.

FindClusters[{e₁, e₂, ...}->{v₁, v₂, ...}]
gives the same result.

FindClusters[{e₁, e₂, ...}, n]
partitions the

into exactly

clusters.

FindClusters[{e₁, e₂, ...}, DistanceFunction->f] treats pairs of elements as being less similar when their distances are larger.

If the are vectors of numbers, FindClusters by default in effect uses the Euclidean distance function EuclideanDistance.

If the are lists of True and False, FindClusters by default uses a distance function based on the normalized fraction of elements that disagree.

If the are strings, FindClusters by default uses a distance function based on the number of point changes needed to get from one string to another.

A Method option can be used to specify different methods of clustering. Possible settings include:

	"Agglomerate"	find clustering hierarchically
	"Optimize"	find clustering by local optimization

Find clusters of nearby values:

Find exactly 4 clusters:

Represent clustered elements with the right-hand sides of each rule: