Ferebee Tunno <ferebee.tunno <at> mathstat.astate.edu> writes:
> Hi everyone -
>
> I know that R is capable of clustering using the k-means algorithm, but can
> R do k-means++ clustering as well?
k-means++ is a routine to suggest center points before the classical k-means
is called. The following lines of code will do that, where X is a matrix of
data points, as requested for kmeans, and k the number of centers:
kmpp <- function(X, k) {
n <- nrow(X)
C <- numeric(k)
C[1] <- sample(1:n, 1)
for (i in 2:k) {
dm <- distmat(X, X[C, ])
pr <- apply(dm, 1, min); pr[C] <- 0
C[i] <- sample(1:n, 1, prob = pr)
}
kmeans(X, X[C, ])
}
Here distmat(a, b) should return the distances between the rows of two
matrices a and b There may be several implementations in R, one is distmat()
in package pracma.
Please note that AFAIK it is not clear whether the approach of kmeans++ is
really better than, e.g., kmeans with several restarts.
Hans Werner
>
> Thanks,
>
> --
> Dr. Ferebee Tunno
> Assistant Professor
> Department of Mathematics and Statistics
> Arkansas State University
> P.O. Box 70
> State University, AR. 72467
> ftu...@astate.edu
> (870) 329-7710
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