ClusterTools Contents

ClusterTools User Guide

dselproto

DSELPROTO

Select prototypes from given dissimilarity matrix

    N = DSELPROTO(D,K,CRIT,NINIT)
    N = D*DSELPROTO(K,CRIT,NINIT)

Input
 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)

Output
 N Indices of selected prototypes

Description

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

This file has been automatically generated. If badly readable, use the help-command in Matlab.