SDKCENTRES k-centres classifier or clusteringP=SDKCENTRES(DATA,options) P=SDKCENTRES(DATA,K) INPUT DATA training dataset K number of centres per class (may be vector) OPTIONS 'k' number of centres per class (required) 'all' execute k-centres on entire data set (not per class) 'iter' number of iterations (opt, def:20) 'maxsamples' max.number of samples used (opt.def:4000) 'cluster' return one output per cluster (default: return one output per class=classifier) 'rounds' repetition rounds to choose best output (minimizing the sum of distances between centers) 'nodisplay' do not show any output OUTPUT P pipeline object DESCRIPTION SDKCENTRES describes data by k centroids (samples). The centroids are selected such that the maximum distance in each corresponding cluster is minimized. By default SDKCENTRES trains a classifier which handles each class in DATA separately and returns one output per class (distance to closest centroid). Data clustering may be performed using 'cluster' option. SDKCENTRES 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#sdkcenters SEE ALSO SDMIXTURE, SDKMEANS

`sdkcentres`

is referenced in examples: