Dissimilarity based clustering by k-centres or k-medoids
LAB = DCLUSTK(D,K,TYPE,L)
LAB = D*DCLUSTK(K,TYPE,L)
| 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', k-centres (default) 'kmedoids', k-medoids|
| L|| Vector length max(K), indices of initial centres. Default: initialisation by DSELPROTO.|
| LAB|| M*N array with the results of the multi-level 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.|
Finds K prototypes from a symmetric distance matrix D. The prototypes are chosen from all M objects such that the maximum (for k-centres) or mean (for k-medoids) 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.
datasets, mappings, kmeans, prkmeans, clustk, dclustm, dclusth, dcluste, clusteval, clustcerr, clustc,
|This file has been automatically generated. If badly readable, use the help-command in Matlab.|