Cluster

Purpose

Agglomerative and K-means cluster analysis with dendrograms..

Synopsis

[results,fig] = cluster(data,labels,options)

[results,fig] = cluster(data,options)

Description

cluster(data) performs a cluster analysis using either one of six different agglomerative methods (including K-Nearest-Neighbor (KNN), furthest neighbor, and Ward's method) or K-means clustering algorithm and plots a dendrogram. The input is data (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 a DataSet object, 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.

Outputs

The outputs are (results) a structure containing results of the clustering (defined below) and the handle (fig) to any plot created. The results structure will contain the following fields:

• dist : the distance threshold at which each cluster forms.

• class : the classes of each sample (columns of class) for each distance (rows of class).

• order : the order of the samples which locates similar samples nearest to each other (this is the order used for the plots).

• linkage : a table of linkages where each row indicates a linkage of one group to another. Each row in the matrix represents one group. The first two columns indicate the sample or group numbers which were linked to form the group. The final column indicates the distance between linked items. Group numbers start at m+1 (where m is the number of samples in the input dat matrix) thus, row j of this matrix is group number m+j. This matrix can be used with the statistics toolbox dendogram function.

The (results.class) matrix can be used with the (results.dist) matrix to determine clusters of samples for any distance using:

 results   = cluster(data);   %do cluster
 ind       = max(find(results.dist<threshold));  %user-desired threshold
 thisclass = results.class(ind,:);   %grab arbitrary classes

Options

options = a structure array with the following fields:

• plots: ['none' | {'final'} ] Governs plotting. When set to 'none', the distance/cluster matrix is returned, 'final' returns a dendrogram plot showing sample distances.
• algorithm: [ ] clustering algorithm. May be one of the following:

'knn' {DEFAULT}: K-Nearest Neighbor

'fn' : Furthest Neighbor

'avgpair' : Average Paired Distance

'med' : Median

'cnt' : Centroid

'ward' : Ward's Method

'kmeans' : K-means

• preprocessing: {[]} Preprocessing structure or keyword (see PREPROCESS),
• pca: [ {'off'} | 'on' ] if 'on' 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},
• mahalanobis: [ {'off'} | 'on' ] if 'on' then a Mahalanobis distance on the scores is used,
• slack: [0] integer number indicating how many samples can be "overridden" when two class branches merge. If the smaller of the two classes has no more than this number of samples, the branch will be absorbed into the larger class. This feature is only valid when classes are supplied in the input data. A value of 0 (zero) disables this feature.

See Also

analysis, corrmap, gcluster, simca