MGC: Mixture Gaussian Clustering

In MultBiplotR: Multivariate Analysis Using Biplots in R

Mixture Gaussian Clustering

Description

Model based clustering using mixtures of gaussian distriutions.

Usage

MGC(x, NG = 2, init = "km", RemoveOutliers=FALSE, ConfidOutliers=0.995, 
tolerance = 1e-07, maxiter = 100, show=TRUE, ...)

Arguments

x	The data matrix
NG	Number of groups or clusters to obtain
init	Initial centers can be obtained from k-means ("km") or at random ("rd")
RemoveOutliers	Should the extreme values be removed to calculate the clusters?
ConfidOutliers	Percentage of the points to keep for the calculations when RemoveOutliers is true.
tolerance	Tolerance for convergence
maxiter	Maximum number of iterations
show	Should the likelihood at each iteration be shown?
...	Maximum number of iterationsAny other parameter that can affect k-means if that is the initial configuration

Details

A basic algorithm for clustering with mixtures of gaussians with no restrictions on the covariance matrices

Value

Clusters

Author(s)

Jose Luis Vicente Villardon

References

Me falta

Examples

X=as.matrix(iris[,1:4])
mod1=MGC(X,NG=3)
plot(iris[,1:4], col=mod1$Classification)
table(iris[,5],mod1$Classification)

MultBiplotR documentation built on Nov. 21, 2023, 5:08 p.m.