perClass Documentation
version 5.1 (11-May-2017)
 SDKMEANS k-means classifier or clustering

    P=SDKMEANS(DATA,options)
    P=SDKMEANS(DATA,K,options)

 INPUT
   DATA        training dataset
   K           number of clusters per class

 OPTIONS
  'k'          number of clusters per class, may be vector (required)
  'all'        execute k-means on entire data set (not per class)
  'iter'       max.number of iterations (opt, def:200)
  'cluster'    return one output per cluster (default: return one
               output per class=classifier)
  'nodisplay'  do not show any output
  'no pruning' do not prune the k-means classifier (returns all trained
               prototypes but yields lower performance)
  'prefix'     Custom cluster name prefix (default: 'C')
  'k final'    k in the final k-NN classifier (default: 1)

 OUTPUT
   P           pipeline object

 DESCRIPTION
 SDKMEANS describes data by means of k clusters. The clusters are defined
 by an iterative algorithm started from randomly selected samples.
 Iterations stop when no samples change their cluster membership or at
 max.number of iterations defined by 'iter' option.
 By default SDKMEANS trains a 1-NN classifier which handles each class in
 DATA separately and returns one output per class (square Euclidean
 distance to the closest cluster).  By default, per-class prototypes are
 pruned removing the ones causing errors on the training set. Use 'no
 pruning' option to return all per-class prototypes.  The k in the final
 k-NN classifier may be changed with 'k final' option.  Data clustering
 may be performed using 'cluster' option.  SDKMEANS then returns one
 output per cluster.  The number of centers may be specified using 'k'
 parameter (vector of 'k', one per class is supported')

 READ MORE
http://perclass.com/doc/guide/clustering.html#sdkmeans

 SEE ALSO
 SDKNN, SDKCENTRES, SDCLUSTER, SDMIXTURE

sdkmeans is referenced in examples: