Built-in Mathematica Symbol

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.

MORE INFORMATION

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

EXAMPLES

CLOSE ALL

Basic Examples (3)

Find clusters of nearby values:

Find exactly four clusters:

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

Find clusters of nearby values:

In[1]:=

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Find exactly four clusters:

In[1]:=

Out[1]=

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

In[1]:=

Out[1]=

Scope (5)

Cluster vectors of real values:

Cluster data of any precision:

Cluster Boolean 0, 1 or True, False data:

Cluster string data:

Find clusters in

five-dimensional vectors:

Options (5)

Use ManhattanDistance as the measure of distance for continuous data:

Clusters obtained with the default SquaredEuclideanDistance:

Use DiceDissimilarity as the measure of distance for Boolean data:

Clusters obtained with the default JaccardDissimilarity:

Use HammingDistance as the measure of distance for string data:

Clusters obtained with the default EditDistance:

Define a distance function as a pure function:

Cluster the data hierarchically:

Clusters obtained with the default method:

Applications (2)

Find and visualize clusters in bivariate data:

Cluster genomic sequences based on the number of element-wise differences:

Properties & Relations (1)

FindClusters groups data while Nearest gives the elements closest to a given value:

Possible Issues (1)

The order of elements can have an effect on the clusters found:

Neat Examples (2)

Divide a square into n segments by clustering uniformly distributed random points:

Cluster words beginning with "ax" in the English dictionary: