SAGMMFit {SAGMM}

Clustering via Stochastic Approximation and Gaussian Mixture Models (GMM)

Description

Fit a GMM via Stochastic Approximation. See Reference.

Usage

SAGMMFit(X, Y = NULL, Burnin = 5, ngroups = 5, kstart = 10,
  plot = FALSE)

Arguments

X

numeric matrix of the data.

Y

Group membership (if known). Where groups are integers in 1:ngroups. If provided ngroups can

Burnin

Ratio of observations to use as a burn in before algorithm begins.

ngroups

Number of mixture components. If Y is provided, and groups is not then is overridden by Y.

kstart

number of kmeans starts to initialise.

plot

If TRUE generates a plot of the clustering.

Value

A list containing

Cluster

The clustering of each observation.

plot

A plot of the clustering (if requested).

l2

Estimate of Lambda^2

ARI1

Adjusted Rand Index 1 - using k-means

ARI2

Adjusted Rand Index 2 - using GMM Clusters

ARI3

Adjusted Rand Index 3 - using intialiation k-means

KM

Initial K-means clustering of the data.

pi

The cluster proportions (vector of length ngroups)

tau

tau matrix of conditional probabilities.

fit

Full output details from inner C++ loop.

Author(s)

Andrew T. Jones and Hien D. Nguyen

References

Nguyen & Jones (2018). Big Data-Appropriate Clustering via Stochastic Approximation and Gaussian Mixture Models. In Data Analytics (pp. 79-96). CRC Press.

Examples

sims<-generateSimData(ngroups=10, Dimensions=10, Number=10^4)
res1<-SAGMMFit(sims$X, sims$Y)
res2<-SAGMMFit(sims$X, ngroups=5)