kmeans18B {T4cluster}

K-Means Clustering with Lightweight Coreset

Description

Apply k-means clustering algorithm on top of the lightweight coreset as proposed in the paper. The smaller the set is, the faster the execution becomes with potentially larger quantization errors.

Usage

kmeans18B(data, k = 2, m = round(nrow(data)/2), ...)

Arguments

data

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

k

the number of clusters (default: 2).

m

the size of coreset (default: n/2).

...

extra parameters including

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

Bachem O, Lucic M, Krause A (2018). “Scalable k -Means Clustering via Lightweight Coresets.” In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining, 1119–1127. ISBN 978-1-4503-5552-0.

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 CORESET SIZES WITH K=3
core1 = kmeans18B(X, k=3, m=25)$cluster
core2 = kmeans18B(X, k=3, m=50)$cluster
core3 = kmeans18B(X, k=3, m=100)$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=core1, pch=19, main="kmeans18B: m=25")
plot(X2d, col=core2, pch=19, main="kmeans18B: m=50")
plot(X2d, col=core3, pch=19, main="kmeans18B: m=100")
par(opar)