[R] Clustering and Rand Index

Mark Hempelmann neo27 at t-online.de
Sat Jan 7 13:38:30 CET 2006

Dear WizaRds,

I am trying to compute the (adjusted) Rand Index in order to comprehend 
the variable selection heuristic (VS-KM) according to Brusco/ Cradit 
2001 (Psychometrika 66 No.2 p.249-270, 2001).

Unfortunately, I am unable to correctly use
cl_ensemble and cl_agreement (package: clue). Here is what I am trying 
to do:


##	Let p1..p4 be four partitions of the kind


Each object within the partitions is assigned to cluster 1,2,3 
respectively. Now I have to create a cl_ensemble object, so that I can 
calculate the Rand index:

ens <- cl_ensemble(list=c(p1,p2,p3,p4))

which only leads to
"Ensemble elements must be all partitions or all hierarchies."

Although I understand that p1..p4 are vectors in this example, they 
represent the partitions I want to use. I don't know how to create the 
necessary partition object in order to transform it into an ensemble 
object, so that I can run cl_agreement - so much transformation, so 
little time...

I have also tried to work around this prbl, creating partitions via 
k-means, but I do not get the same partitions I need to validate. I am 
sure the following algorithm needs improvement, especially the use of 
putting matrices into a list through a for loop (ouch) - I am very 
grateful for your comments of improving this terrible piece of R-work 
(is it easier to do sthg with apply?).

Thank you very much for your help and support

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="" )

clus.mat <- vector(mode="list", length=4)
for (i in 1:4){
	clus.mat[[i]] <- kmeans(mat[i,], centers=3, nstart=1, 
algorithm="MacQueen") ## run kmeans on each row (clustering per single 


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