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BUILT-IN MATHEMATICA SYMBOL
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 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
