Hi R users!

I have a bit of a problem with using an hierarchical clustering algorithm:

 a<-c(1:15)
 b<-rep(seq(1:3), 5)
 c<-rnorm(15, 0,1)
 d<-c(sample(1:100, 15, replace=T))
 e<-c(sample(1:100, 15, replace=T))
 f<-c(sample(1:100, 15, replace=T))
 data<-data.frame(a,b,c,d,e,f)
 q<-data.frame(data$d, data$e, data$f)
 q<-scale(q)


What i want to do is to use an hierarchical cluster analysis on q data.frame, but using data$c as a weighting variable, could it be done? or is there a package that would  let me use my weights in the clustering process, but an hierarchical process?

Another question:
say i wanted to t.test data$d, data$e but having again data$c as weights, how could it be done? 

and the last 2 questions:
1. how can i weight a whole dataframe in order for me to keep my weights for a specific analysis, like cluster or t.test or any other analysis that does not let me incorporate a "weight" option? I am looking for something like in spss where i can weight a whole data frame and use it for a subsequent analysis, or something like the survey package from R but one that offers flexibility to use any analysis that i want (i saw that survey package offers limited connectivity  to such  analyses ) 
 2. why does a kmeans cluster analysis offer a  multitude  of different results?
I tried both several times 
>cclust(scale(q), 3, verbose=T)
>kmeans(scale(q), 3)
 and they both seem vary unstable even with this small data.frame with respect to the cluster sizing, and i don't  know why? Does it always behave  like this ? 

Thank you and have a great day!!

       
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