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Select prototypes from given dissimilarity matrix


 D Dissimilarity matrix  between objects (rows) and protoypes (columns)
 K Number of prototypes to be selected
 CRIT 'kmeans', 'kmedoids' or 'kcentres' (default: 'kmeans')
 NINIT Indices of preselected prototypes (optional)

 N Indices of selected prototypes


Select and rank a subset of K prototypes (columns) of the matrix D using  a greedy forward selection approach such that the maximum NN distance  from all objects to these prototypes minimizes

  • for CRIT is 'kmeans' or 'kmedoids': mean(min(D(:,N(1:K),[],2)).

- for CRIT is 'kcentres' : max(min(D(:,N(1:K),[],2)).

This routines tries to sample the given prototypes (possibly the original  objects) such that they are evenly spaced judged from the dissimilarities.  This may be used as a systematic initialisation in kmeans, kmedoids or  kcentres procedures.

The dissimilarity matrix D might be rectangular (prototypes different  from objects). It might also be asymmetric.

See also

datasets, mappings, selproto,

ClusterTools Contents

ClusterTools User Guide

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