gmm03F {T4cluster}

Ensemble of Gaussian Mixtures with Random Projection

Description

When the data lies in a high-dimensional Euclidean space, fitting a model-based clustering algorithm is troublesome. This function implements an algorithm from the reference, which uses an aggregate information from an ensemble of Gaussian mixtures in combination with random projection.

Usage

gmm03F(data, k = 2, ...)

Arguments

data

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

k

the number of clusters (default: 2).

...

extra parameters including

nruns

the number of projections (default: 20).

lowdim

target dimension for random projection (default: 5).

maxiter

the maximum number of iterations (default: 10).

usediag

a logical; covariances are diagonal if TRUE, or full covariances are returned for FALSE (default: FALSE).

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

Fern XZ, Brodley CE (2003). “Random Projection for High Dimensional Data Clustering: A Cluster Ensemble Approach.” In Proceedings of the Twentieth International Conference on International Conference on Machine Learning, ICML'03, 186–193. ISBN 1577351894.

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 = gmm03F(X, k=2)$cluster
cl3 = gmm03F(X, k=3)$cluster
cl4 = gmm03F(X, k=4)$cluster

## VISUALIZATION
opar <- par(no.readonly=TRUE)
par(mfrow=c(2,2), pty="s")
plot(X2d, col=lab, pch=19, main="true label")
plot(X2d, col=cl2, pch=19, main="gmm03F: k=2")
plot(X2d, col=cl3, pch=19, main="gmm03F: k=3")
plot(X2d, col=cl4, pch=19, main="gmm03F: k=4")
par(opar)