Hello,
I am doing clustering and I want to know how can i find  Silhouette width
using K-means. Just like PAM (code below).
(2) Secondly, I have mixed data all sort of variables numeric, categorical,
nominals so I first change to all nominal to binary and
normlise the data before any clustering. Is there any other elegant way of
doing this?
(3) another question how to normlise and change to binary (filters) in R?

Thank you in Advance.

ravi

Examples(PAM)

## Use the silhouette widths for assessing the best number of clusters,
## following a one-dimensional example from Christian Hennig :
##
x <- c(rnorm(50), rnorm(50,mean=5), rnorm(30,mean=15))
asw <- numeric(20)
## Note that "k=1" won't work!
for (k in 2:20)
  asw[k] <- pam(x, k) $ silinfo $ avg.width
k.best <- which.max(asw)
cat("silhouette-optimal number of clusters:", k.best, "\n")

plot(1:20, asw, type= "h", main = "pam() clustering assessment",
     xlab= "k  (# clusters)", ylab = "average silhouette width")
axis(1, k.best, paste("best",k.best,sep="\n"), col = "red", col.axis =
"red")

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