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18 classifdist<br />
Details<br />
classifdist is <strong>for</strong> data given as dissimilarity matrix, classifnp is <strong>for</strong> data given as n times p data<br />
matrix. The following methods are supported:<br />
"centroid" assigns observations to the cluster with closest cluster centroid as specified in argument<br />
centroids (this is associated to k-me<strong>an</strong>s <strong>an</strong>d pam/clara-clustering).<br />
"averagedist" assigns to the cluster to which <strong>an</strong> observation has the minimum average dissimilarity<br />
to all points in the cluster (this is associated to average linkage clustering).<br />
"qda" only in classifnp. Classifies by quadratic discrimin<strong>an</strong>t <strong>an</strong>alysis (this is associated to Gaussi<strong>an</strong><br />
clusters with flexible covari<strong>an</strong>ce matrices), calling qda with default settings. If qda gives<br />
<strong>an</strong> error (usually because a class was too small), lda is used.<br />
"knn" classifies by nnk nearest neighbours (<strong>for</strong> nnk=1, this is associated with single linkage clustering).<br />
Calls knn in classifnp.<br />
Value<br />
An integer vector giving cluster numbers <strong>for</strong> all observations; those <strong>for</strong> the observations already<br />
clustered in the input are the same as in the input.<br />
Author(s)<br />
Christi<strong>an</strong> Hennig http://www.homepages.ucl.ac.uk/~ucakche/<br />
See Also<br />
prediction.strength, nselectboot<br />
Examples<br />
set.seed(20000)<br />
x1
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