kmeanspp: kmeans++ clustering

Description

Use the kmeans++ algorithm to cluster points into k clusters, as implemented in the deprecated LICORS package, using the built-in function kmeans.

Usage

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

Value

A list with 9 entries:

cluster: A vector of integers from 1:k indicating the cluster to which each point is allocated.
centers: A matrix of cluster centers.
totss: The total sum of squares.
withinss: Vector of within-cluster sum of squares, one component per cluster.
tot.withinss: Total within-cluster sum of squares, i.e.sum(withinss).
betweenss: The between-cluster sum of squares, i.e.totss-tot.withinss.
size: The number of points in each cluster.
iter: The number of (outer) iterations.
ifault: An integer indicator of a possible algorithm problem.
initial.centers: The initial centers used.

Arguments

data: An \(N \times d\) matrix, where there are \(N\) samples in dimension \(d\).
k: The number of clusters.
iter.max: The maximum number of iterations.
nstart: The number of restarts.
...: 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.

Examples

Run this code

set.seed(1984)
n <- 100
X = matrix(rnorm(n), ncol = 2)
Y = matrix(runif(length(X)*2, -1, 1), ncol = ncol(X))
Z = rbind(X, Y)
cluster_Z = kmeanspp(Z, k = 5)