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 n clusters.

Details and OptionsDetails and Options

  • 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.
  • For images, FindClusters[{img1, img2, ...}, DistanceFunction->f] effectively uses DistanceFunction->(ImageDistance[#1, #2, DistanceFunction->f]&).
  • 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
New in 6 | Last modified in 9
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