SAGMMFit: Clustering via Stochastic Approximation and Gaussian Mixture...

In SAGMM: Clustering via Stochastic Approximation and Gaussian Mixture Models

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)

SAGMM documentation built on June 29, 2019, 9:02 a.m.