CLUSTER

The CLUSTER function computes the classification of an n-column, m-row array, where n is the number of variables and m is the number of observations or samples. CLUST_WTS uses k-means clustering. With this technique, CLUST_WTS starts with k random clusters and then iteratively moves items between clusters, minimizing variability within each cluster and maximizing variability between clusters.

Note
Because the initial clusters are chosen randomly, your results may differ slightly each time the CLUST_WTS routine is invoked, even for the same input data. For data with well-defined clusters the differences should be slight. For randomly-scattered data (no distinguishable clusters), the results may be significantly different, which may indicate that k-means clustering is not appropriate for your data.

Tip
For hierarchical tree clustering, see the CLUSTER_TREE function.

For more information on cluster analysis, see:

Everitt, Brian S. Cluster Analysis. New York: Halsted Press, 1993. ISBN 0-470-22043-0

Syntax

Result = CLUSTER( Array, Weights [, /DOUBLE] [, N_CLUSTERS=value] )

Return Value

Results in a 1-column, m-row array of cluster number assignments that correspond to each sample.

Arguments

Array

An n-column, m-row array of type float or double.

Weights

An array of weights (the cluster centers) computed using the CLUST_WTS function. The dimensions of this array vary according to keyword values.

Keywords

DOUBLE

Set this keyword to force the computation to be done in double-precision arithmetic.

N_CLUSTERS

Set this keyword equal to the number of clusters. The default is based upon the row dimension of the Weights array.

Examples

; Construct 3 separate clusters in a 3D space:
n = 50
c1 = RANDOMN(seed, 3, n)
c1[0:1,*] -= 3
c2 = RANDOMN(seed, 3, n)
c2[0,*] += 3
c2[1,*] -= 3
c3 = RANDOMN(seed, 3, n)
c3[1:2,*] += 3
array = [[c1], [c2], [c3]]

; Compute cluster weights, using three clusters:
weights = CLUST_WTS(array, N_CLUSTERS = 3)

; Compute the classification of each sample:
result = CLUSTER(array, weights, N_CLUSTERS = 3)

; Plot each cluster using a different symbol:
IPLOT, array[*, WHERE(result eq 0)], $
   LINESTYLE = 6, SYM_INDEX = 2
IPLOT, array[*, WHERE(result eq 1)], /OVERPLOT, $
   LINESTYLE = 6, SYM_INDEX = 4
IPLOT, array[*, WHERE(result eq 2)], /OVERPLOT, $
   LINESTYLE = 6, SYM_INDEX = 1

Figure 5-2: CLUSTER Example

Version History


5.0	Introduced