K-Means Clustering

\(K\)-means algorithm we provide is a wrapper to the Armadillo's k-means routine. Two types of initialization schemes are employed. Please see the parameters section for more details.

kmeans(data, k = 2, ...)

Arguments

data	an \((n\times p)\) matrix of row-stacked observations.
k	the number of clusters (default: 2).
...	extra parameters including init initialization method; either "random" for random initialization, or "plus" for k-means++ starting. maxiter the maximum number of iterations (default: 10). nstart the number of random initializations (default: 5).

Value

a named list of S3 class T4cluster containing

cluster

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

mean

a \((k\times p)\) matrix where each row is a class mean.

wcss

within-cluster sum of squares (WCSS).

algorithm

name of the algorithm.

References

Sanderson C, Curtin R (2016). “Armadillo: A Template-Based C++ Library for Linear Algebra.” The Journal of Open Source Software, 1(2), 26. ISSN 2475-9066.

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 = kmeans(X, k=2)$cluster
cl3 = kmeans(X, k=3)$cluster
cl4 = kmeans(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)