haclust

Hierarchical-agglomerative cluster analysis.

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Usage 1

haclust(d, m)

d

A dissimilarity or distance matrix. For N data points this is a NxN matrix (e.g. created using the function dist)

m

The cluster method:

m	method	0	Single Linkage
1	Complete Linkage
2	Weighted Average Linkage
3	Average Linkage
4	Median
5	Centroid
6	Ward

Result 1

The result is a cluster hierarchy. This is a 3x(N-1) table containing the merged cluster pairs and the agglomeration niveaus.

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Usage 2

haclust(h, c)

h

A cluster hierarchy (e.g. computed with Usage 1).

c

The number of clusters.

Result

The created partition, which is a 1xN table containing the group indices.

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See also: modclust

Example:

// A simple demonstration of <code>haclust</code> usage.
[macro haclust_example]
// create bivariate input data
#datatable := new table * 2 /p num:x num:y
$#datatable[*,0] := '1;2;1;3;4.5'
$#datatable[*,1] := '1;0.5;4;2;4'
        
// compute distance matrix
#disttable := eval dist($#datatable)

// cluster analysis methods:
// 0 (SL), 1 (CL), 2 (WAL), 3 (AL), 4 (M), 5 (C), 6 (W)
#method := 6    // Ward

// nr of clusters
#nclusters := 2

// compute hierarchy and partition
#hierarchy := eval haclust($#disttable,$#method)
#partition := eval haclust($#hierarchy,$#nclusters)

// show results
showitem $#hierarchy
showitem $#partition
exit