Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

Group: Cluster analysis
See also: tree kmeans

Function: agglom
Description: performs hierarchical cluster analysis.

Usage: cagg = agglom (d, method, no{, opt})
Input:
d n x 1 vector or l x l matrix of distances
method string, one of: "WARD", "SINGLE", "COMPLETE", "MEAN_LINK", "MEDIAN_LINK", "AVERAGE", "CENTROID" or "LANCE".
no scalar, number of clusters
opt optional argument for some methods - see note below
Output:
cagg.p l x 1 matrix with partition numbers (1,2,...)
cagg.t p x 2 matrix with the dendrogram for no clusters
cagg.g p x 2 matrix with the dendrogram for all l clusters
cagg.pd l x 1 matrix with with partition numbers (1,2,...)
cagg.d no x (no-1)/2 matrix with distances between the cluster centers

Note:

Example:



proc()=main()

  ; load the swiss banknote data

  x=read("bank2")

  ; compute the euclidean distance between banknotes

  i=0

  d=0.*matrix(rows(x),rows(x))

  while (i.<cols(x))

    i = i+1

    d = d+(x[,i] - x[,i]')^2

  endo

  d = sqrt(d) 

  ; use the WARD method to cluster the data

  t = agglom (d, "WARD", 3)

  t.p

endp



;

main()

Result:



//gives the partition of the data into 3 clusters

Contents of p

[  1,]        1 

[  2,]        1 

[  3,]        1 

[  4,]        1 

[  5,]        1 

[  6,]        1 

[  7,]        1 

...

[ 98,]        1 

[ 99,]        1 

[100,]        1 

[101,]        2 

[102,]        2 

[103,]        2 

[104,]        2 

[105,]        3 

[106,]        2 

[107,]        2 

...

[194,]        2 

[195,]        3 

[196,]        2 

[197,]        2 

[198,]        2 

[199,]        2 

[200,]        2 


Group: Cluster analysis
See also: tree kmeans

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 21.9.2000