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

 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

Example:
```; 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
```gives a partition ca.b of n row points into 3 clusters which