clusterdata (Statistics Toolbox)

Construct clusters from data.

Syntax

T = clusterdata(X,cutoff)

Description

T = clusterdata(X,cutoff)constructs clusters from the data matrix X. X is a matrix of size m by n, interpreted as m observations of n variables.

cutoff is a threshold value that determines how the cluster function creates clusters. The value of cutoff determines how clusterdata interprets it


Value	Meaning
`0 < cutoff < 1`	`cutoff` is interpreted as the threshold for the inconsistency coefficient. The inconsistency coefficient quantifies the degree of difference between objects in the hierarchical cluster tree. If inconsistency coefficient of a link is greater than the threshold, the `cluster` function uses the link as a boundary for a cluster grouping. For more information about the inconsistency coefficient, see the `inconsistent` function.
`cutoff >= 1`	`cutoff` is interpreted as the maximum number of clusters to keep in the hierarchical tree.

The output, T, is a vector of size m that identifies, by number, the cluster in which each object was grouped.

T = clusterdata(X,cutoff) is the same as

Y = pdist(X,'euclid');
Z = linkage(Y,'single');
T = cluster(Z,cutoff);

Follow this sequence to use nondefault parameters for pdist and linkage.

Example

The example first creates a sample dataset of random numbers. The example then uses the clusterdata function to compute the distances between items in the dataset and create a hierarchical cluster tree from the dataset. Finally, the clusterdata function groups the items in the dataset into three clusters. The example uses the find function to list all the items in cluster 2.

rand('seed', 12); 
X = [rand(10,3); rand(10,3)+1.2; rand(10,3)+2.5; 
T = clusterdata(X,3); 
find(T == 2)

ans =


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