dclustk
DCLUSTK
Dissimilarity based clustering by kcentres or kmedoids
LAB = DCLUSTK(D,K,TYPE,L)
LAB = D*DCLUSTK(K,TYPE,L)
Input  D  Square dissimilarity matrix, size M*M  K  Vector of length N with desired numbers of clusters, default is a sampling of [2:M]  TYPE  'kcentres', kcentres (default) 'kmedoids', kmedoids  L  Vector length max(K), indices of initial centres. Default: initialisation by DSELPROTO. 
Output  LAB  M*N array with the results of the multilevel clusterings for the  M  objects. The columns refer to the N clusterings. They yield for the objects the prototype indices of the clusters they belong to. 
Description Finds K prototypes from a symmetric distance matrix D. The prototypes are chosen from all M objects such that the maximum (for kcentres) or mean (for kmedoids) of the distances over all objects to the nearest prototype is minimized.
In case K is a vector (length N), the procedure is repeated for as set of values for K between 2 and M. See also
datasets, mappings, kmeans, prkmeans, clustk, dclustm, dclusth, dcluste, clusteval, clustcerr, clustc, This file has been automatically generated. If badly readable, use the helpcommand in Matlab. 
