PLS_Toolbox Documentation: cluster	< class2logical	coadd >

cluster

Purpose

K-means and K-nearest neighbor cluster analysis with dendrograms.

Synopsis

cluster(data,labels,options)

cluster(data,options)

options = cluster('options')

Description

cluster(data) performs a cluster analysis on data matrix data using K-means or K-nearest neighbor clustering and plots a dendrogram showing distances between the samples. data can be class "double" or "dataset".

Optional input labels can be used to put labels on the dendrogram plots. For data M by N then labels must be a character array with M rows. When labels is not specified and data is class "double", the dendrogram is plotted using sample numbers. When labels is not specified and data is class "dataset", the dendrogram is plotted using sample labels. If the labels field is empty it will use sample numbers.

The output is a dendrogram showing the sample distances.

Note: Calling cluster with no inputs starts the graphical user interface (GUI) for this analysis method.

Options

             options =   a structure array with the following fields:

                     name:   'options', name indicating that this is an options structure,

           algorithm:   [ {'knn'} | 'kmeans' ] clustering algorithm,

   preprocessing:   {[]} Preprocessing structure or keyword (see PREPROCESS),

                       pca:   [ {'false'} | 'true' ] if 'true' then CLUSTER performs PCA first and clustering on the scores,

                   ncomp:   [] number of PCA factors to use {default = [], the user is prompted to select the number of factors from the SSQ table}, and

           mahalanobis:   [ {'false'} | 'true' ] if 'true' then a Mahalanobis distance on the scores is used.

The default options can be retreived using: options = cluster('options');.

See Also

analysis, corrmap, gcluster, simca