DP-Means Clustering

DP-means is a non-parametric clustering method motivated by DP mixture model in that the number of clusters is determined by a parameter \(\lambda\). The larger the \(\lambda\) value is, the smaller the number of clusters is attained. In addition to the original paper, we added an option to randomly permute an order of updating for each observation's membership as a common heuristic in the literature of cluster analysis.

dpmeans(data, lambda = 0.1, ...)

Arguments

data	an \((n\times p)\) matrix of row-stacked observations.
lambda	a threshold to define a new cluster (default: 0.1).
...	extra parameters including maxiter the maximum number of iterations (default: 10). eps the stopping criterion for iterations (default: 1e-5). permute a logical; TRUE if random order for update is used, FALSE otherwise (default).

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

Kulis B, Jordan MI (2012). “Revisiting K-Means: New Algorithms via Bayesian Nonparametrics.” In Proceedings of the 29th International Coference on International Conference on Machine Learning, ICML'12, 1131--1138. ISBN 978-1-4503-1285-1.

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 LAMBDA VALUES
dpm1 = dpmeans(X, lambda=1)$cluster
dpm2 = dpmeans(X, lambda=5)$cluster
dpm3 = dpmeans(X, lambda=25)$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=dpm1, pch=19, main="dpmeans: lambda=1")
plot(X2d, col=dpm2, pch=19, main="dpmeans: lambda=5")
plot(X2d, col=dpm3, pch=19, main="dpmeans: lambda=25")
par(opar)