LICORS (version 0.2.0)

kmeanspp: Kmeans++

Description

kmeans++ clustering (see References) using R's built-in function kmeans.

Usage

kmeanspp(data, k = 2, start = "random", iter.max = 100, nstart = 10, ...)

Arguments

data

an \(N \times d\) matrix, where \(N\) are the samples and \(d\) is the dimension of space.

k

number of clusters.

start

first cluster center to start with

iter.max

the maximum number of iterations allowed

nstart

how many random sets should be chosen?

...

additional arguments passed to kmeans

References

Arthur, D. and S. Vassilvitskii (2007). ``k-means++: The advantages of careful seeding.'' In H. Gabow (Ed.), Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms [SODA07], Philadelphia, pp. 1027-1035. Society for Industrial and Applied Mathematics.

See Also

kmeans

Examples

# NOT RUN {
set.seed(1984)
nn <- 100
XX <- matrix(rnorm(nn), ncol = 2)
YY <- matrix(runif(length(XX) * 2, -1, 1), ncol = ncol(XX))
ZZ <- rbind(XX, YY)

cluster_ZZ <- kmeanspp(ZZ, k = 5, start = "random")

plot(ZZ, col = cluster_ZZ$cluster + 1, pch = 19)
# }