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# ClusterPointsKmeans

## PURPOSE

[index,nclusters] = ClusterPointsKmeans(data,distance,nclusters)

## SYNOPSIS

function[index,nclusters] = ClusterPointsKmeans(data,distance,nclusters)

## DESCRIPTION

     [index,nclusters] = ClusterPointsKmeans(data,distance,nclusters)
     ClusterPointsKmeans:  Clusters a data set using Affinity propagation
                             and a given distance. The number of clusters
                             is given.
     INPUT
     data: A vector of data were rows are observations and columns are
     features
     distance: Distance used for clustering (e.g. 'euclidean', 'correlation', 'cosine' ... See help pdist for full list of
                              possible metrics)
     nclusters: Number of  clusters.
     OUTPUT
     index: Cluster each solution belongs to
     nclusters: Number of clusters

     Example
     [index,nclusters] = ClusterPointsKmeans(rand(50,10),'sqEuclidean',5);

     Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)

## CROSS-REFERENCE INFORMATION

This function calls:

This function is called by:

## SOURCE CODE

    0001 function[index,nclusters] = ClusterPointsKmeans(data,distance,nclusters)
    0002 % [index,nclusters] = ClusterPointsKmeans(data,distance,nclusters)
    0003 % ClusterPointsKmeans:  Clusters a data set using Affinity propagation
    0004 %                         and a given distance. The number of clusters
    0005 %                         is given.
    0006 % INPUT
    0007 % data: A vector of data were rows are observations and columns are
    0008 % features
    0009 % distance: Distance used for clustering (e.g. 'euclidean', 'correlation', 'cosine' ... See help pdist for full list of
    0010 %                          possible metrics)
    0011 % nclusters: Number of  clusters.
    0012 % OUTPUT
    0013 % index: Cluster each solution belongs to
    0014 % nclusters: Number of clusters
    0015 %
    0016 % Example
    0017 % [index,nclusters] = ClusterPointsKmeans(rand(50,10),'sqEuclidean',5);
    0018 %
    0019 % Last version 8/26/2008. Roberto Santana (roberto.santana@ehu.es)
    0020
    0021 [index,c,sumd,d] = kmeans(data,nclusters,'Distance',distance,'EmptyAction','drop');
    0022
    0023 nclusters = max(index);
    0024 return
    0025
    0026
    0027
    0028
    0029
    0030
    0031
    0032