T4cluster (version 0.1.2)

gmm: Finite Gaussian Mixture Model

Description

Finite Gaussian Mixture Model (GMM) is a well-known probabilistic clustering algorithm by fitting the following distribution to the data $$f(x; \left\lbrace \mu_k, \Sigma_k \right\rbrace_{k=1}^K) = \sum_{k=1}^K w_k N(x; \mu_k, \Sigma_k)$$ with parameters \(w_k\)'s for cluster weights, \(\mu_k\)'s for class means, and \(\Sigma_k\)'s for class covariances. This function is a wrapper for Armadillo's GMM function, which supports two types of covariance models.

Usage

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

Arguments

data

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

k

the number of clusters (default: 2).

...

extra parameters including

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 \(1:k\)).

mean

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

variance

a \((p\times p\times k)\) array where each slice is a class covariance.

weight

a length-\(k\) vector of class weights that sum to 1.

loglkd

log-likelihood of the data for the fitted model.

algorithm

name of the algorithm.

Examples

# NOT RUN {
# -------------------------------------------------------------
#            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 = gmm(X, k=2)$cluster
cl3 = gmm(X, k=3)$cluster
cl4 = gmm(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="gmm: k=2")
plot(X2d, col=cl3, pch=19, main="gmm: k=3")
plot(X2d, col=cl4, pch=19, main="gmm: k=4")
par(opar)

# }