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FindClusters

FindClusters
partitions the into clusters of similar elements.
FindClusters
returns the corresponding to the in each cluster.
FindClusters
gives the same result.
FindClusters
partitions 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:
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Click for copyable input
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Find exactly four clusters:
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Click for copyable input
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Represent clustered elements with the right-hand sides of each rule:
In[1]:=
Click for copyable input
Out[1]=
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:
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:
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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