MGC {MultBiplotR}

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)