kmeanspp {motifcluster}

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, ...)

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.

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.

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)
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)