ClusterTools Contents

ClusterTools User Guide



Dissimilarity based clustering by k-centres or k-medoids


 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.

See also

datasets, mappings, kmeans, prkmeans, clustk, dclustm, dclusth, dcluste, clusteval, clustcerr, clustc,

ClusterTools Contents

ClusterTools User Guide

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