It might be easiest to just calculate the means of the (raw) data once you have the cluster assignments. For example, using plyr:
# install.packages('plyr')
require(plyr)
dat <- mtcars[,1:4]
dat$cvar <- kmeans(scale(dat), 3)$cluster
ddply(dat, c("cvar"), colwise(mean))
cvar mpg cyl disp hp
1 1 13.41429 8.000000 390.5714 248.42857
2 2 23.97222 4.777778 135.5389 98.05556
3 3 16.78571 8.000000 315.6286 170.00000