# kmeanspp: Kmeans++

### Description

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

### Usage

 1 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.

kmeans
 1 2 3 4 5 6 7 8 9 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)