gives an array in which each element at the lowest level of array is replaced by an integer index representing the cluster in which the element lies.
finds at most n clusters.
ClusteringComponents[array, n, level]
finds clusters at the specified level in array.
finds clusters of pixels with similar values in image.
finds at most n clusters in image.
- ClusteringComponents[source, DistanceFunction->f] treats pairs of elements, in source as being less similar when their distances are larger.
- For images and real data, ClusteringComponents by default effectively uses the Euclidean distance function EuclideanDistance to determine the similarity of elements.
- For lists of True and False, ClusteringComponents by default uses a distance function based on the normalized fraction of elements that disagree.
- For lists of strings, ClusteringComponents by default uses a distance function based on the number of point changes needed to get from one string to another.
- 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.
- ClusteringComponents also works with Image3D objects.