Kmeans++
kmeans++ clustering (see References) using R's
built-in function kmeans.
kmeanspp(data, k = 2, start = "random", iter.max = 100, nstart = 10, ...)
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
|
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.
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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.