[R] Clustering and Rand Index - VS-KM

[Mark Hempelmann]

Dear WizaRds,

I have been trying to compute the adjusted Rand index as by Hubert/ 
Arabie, and could not correctly approach how to define a partition 
object as in my last request yesterday.

With package fpc I try to work around the problem, using my original data:

mat <- matrix( c(6,7,8,2,3,4,12,14,14, 14,15,13,3,1,2,3,4,2, 
15,3,10,5,11,7,13,6,1, 15,4,10,6,12,8,12,7,1), ncol=9, byrow=T )
rownames(mat) <- paste("v", 1:4, sep="" )

## and the given partitions:

p1=c(1,1,1,2,2,2,3,3,3)
p2=c(1,1,1,3,2,2,3,3,2)
p3=c(1,2,1,3,1,3,1,3,2)
p4=c(1,2,1,3,1,3,1,3,2)

## Now

cluster.stats(d=dist(mat), clustering=p1, alt.clustering=p2)

## just gives
Error in as.dist(dmat[clustering == i, clustering == i]) :
	(subscript) logical subscript too long

I think I don't understand the use of 'd' here. How can I calculate the 
corrected Rand matrix:
( .000  .407 -.071 -.071)
( .407  .000 -.071 -.071)
(-.071 -.071  .000 1.000)
(-.071 -.071 1.000  .000)

Does the clue package help me here? Does anyone know if there is a VS-KM 
algorithm (Variable Selection Heuristic for K-Means Clustering) 
implemented in R? Unfortunately, I did not find any serach entries.

Thank you for your help and support
Mark