kmeanspp {T4cluster}

K-Means++ Clustering

Description

K-means++ algorithm is usually used as a fast initialization scheme, though it can still be used as a standalone clustering algorithms by first choosing the centroids and assign points to the nearest centroids.

Usage

kmeanspp(data, k = 2)

Arguments

data

an (n \times p) matrix of row-stacked observations.

k

the number of clusters (default: 2).

Value

a named list of S3 class T4cluster containing

cluster

a length-n vector of class labels (from 1:k).

algorithm

name of the algorithm.

References

Arthur D, Vassilvitskii S (2007). “K-Means++: The Advantages of Careful Seeding.” In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA '07, 1027–1035. ISBN 978-0-89871-624-5.

Examples

# -------------------------------------------------------------
#            clustering with 'iris' dataset
# -------------------------------------------------------------
## PREPARE
data(iris)
X   = as.matrix(iris[,1:4])
lab = as.integer(as.factor(iris[,5]))

## EMBEDDING WITH PCA
X2d = Rdimtools::do.pca(X, ndim=2)$Y

## CLUSTERING WITH DIFFERENT K VALUES
cl2 = kmeanspp(X, k=2)$cluster
cl3 = kmeanspp(X, k=3)$cluster
cl4 = kmeanspp(X, k=4)$cluster

## VISUALIZATION
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,4), pty="s")
plot(X2d, col=lab, pch=19, main="true label")
plot(X2d, col=cl2, pch=19, main="k-means++: k=2")
plot(X2d, col=cl3, pch=19, main="k-means++: k=3")
plot(X2d, col=cl4, pch=19, main="k-means++: k=4")
par(opar)