| Library: | xclust |
| See also: | adap |
| Quantlet: | adaptive | |
| Description: | performs an adaptive K-means cluster analysis with appropriate (adaptive) multivariate graphic using the principal components |
| Usage: | ca = adaptive(x, k, w, m, t) | |
| Input: | ||
| x | n x p matrix of n row points to be clustered | |
| k | scalar the number of clusters | |
| w | p x 1 matrix of weights of column points | |
| m | n x 1 matrix of weights (masses) of row points | |
| t | n x 1 matrix of the true partition (only if known, else a matrix containing 1) | |
| Output: | ||
| ca.b | n x 1 matrix partition of n points into k clusters | |
| ca.c | k x p matrix of means (centroids) of clusters | |
| ca.v | k x p matrix of within cluster variances divided by the corresponding weights (masses) of clusters | |
| ca.s | k x 1 matrix of weights (masses) of clusters | |
| ca.a | p x 1 matrix of adaptive weights of variables | |
; load the library xclust
library("xclust")
; initialize random generator
randomize(0)
; generate some normal data
x = normal(200, 5)
x1 = x - #(2,1,3,0,0)'
x2 = x + #(1,1,3,1,0.5)'
x3 = x + #(0,0,1,5,1)'
; make one data set
x = x1|x2|x3
; compute column variances
w = 1./var(x)
; generate row weights(here : 1)
m = matrix(rows(x))
; generate true partition
t = matrix(200)|matrix(200).+1|matrix(200).+2
; apply adaptive clustering
ca = adaptive(x, 3, w, m, t)
gives a partition ca.b of n row points into 3 clusters which minimizes the sum of within cluster variances according to the column weights (1/pooled within cluster variances)