# gmm03F: Ensemble of Gaussian Mixtures with Random Projection In T4cluster: Tools for Cluster Analysis

## 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

 1 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 nrunsthe number of projections (default: 20). lowdimtarget dimension for random projection (default: 5). maxiterthe maximum number of iterations (default: 10). usediaga 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

\insertRef

10.5555/3041838.3041862T4cluster

## Examples

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 # ------------------------------------------------------------- # 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) 

T4cluster documentation built on Aug. 16, 2021, 9:07 a.m.