WOLFRAM

Enable JavaScript to interact with content and submit forms on Wolfram websites. Learn how

Legacy Documentation

Mathematica 8 (2010)

This is documentation for Mathematica 8, which was
based on an earlier version of Wolfram Language.
View current documentation (Version 14.3)

ClusteringComponents

ClusteringComponents [array]
gives an array in which each element of array is replaced by an integer index representing the cluster in which the element lies.
ClusteringComponents
finds at most n clusters.
ClusteringComponents
finds clusters at the specified level in array.
ClusteringComponents [image]
finds clusters of pixels with similar values in image.
ClusteringComponents
finds at most n clusters in image.
  • Other distance functions can be specified by setting the DistanceFunction option. Possible settings are:
ManhattanDistance Manhattan or "city block" distance
EuclideanDistance Euclidean distance
SquaredEuclideanDistance squared Euclidean distance
NormalizedSquaredEuclideanDistance normalized squared Euclidean distance
CosineDistance angular cosine distance
CorrelationDistance correlation coefficient distance
  • 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
"KMeans" -means clustering algorithm
"PAM" find clustering by partitioning around medoids
  • ClusteringComponents accepts a option that is used to control the creation of the initial set of seeds.
(4)
Label two clusters of values in a list:
Clustering transform of nested lists:
Cluster analysis of an MR image:
Find a color segmentation of a satellite image:
Label two clusters of values in a list:
Out[1]=
Clustering transform of nested lists:
Out[1]=
Cluster analysis of an MR image:
Out[1]=
Find a color segmentation of a satellite image:
Out[1]=
(6)
Clusters of values in a matrix:
Find color clusters in an image:
Find clusters at list level 2:
Find clusters at list level 1:
Find duplicates by specifying a large number of clusters:
Labeling clusters in a matrix:
(1)
Color segmentation of a microscopic image, after smoothing with a Perona-Malik filter:
New in 8


ja zh

AltStyle によって変換されたページ (->オリジナル) /