This is documentation for Mathematica 8, which was
based on an earlier version of the Wolfram Language.

# FindClusters

 FindClusterspartitions the into clusters of similar elements. FindClustersreturns the corresponding to the in each cluster. FindClustersgives the same result. FindClusterspartitions the into exactly n clusters.
• 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 four clusters:
Represent clustered elements with the right-hand sides of each rule:
Find clusters of nearby values:
 Out[1]=

Find exactly four clusters:
 Out[1]=

Represent clustered elements with the right-hand sides of each rule:
 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:
FindClusters groups data while Nearest gives the elements closest to a given value:
The order of elements can have an effect on the clusters found:
Divide a square into n segments by clustering uniformly distributed random points:
Cluster words beginning with "ax" in the English dictionary:
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