kmeanspp: Kmeans++

In LICORS: Light Cone Reconstruction of States - Predictive State Estimation From Spatio-Temporal Data

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

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)

Example output

LICORS documentation built on May 1, 2019, 10:13 p.m.