MGC function

Mixture Gaussian Clustering

Model based clustering using mixtures of gaussian distriutions.

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

Returns

Clusters

References

Me falta

Author(s)

Jose Luis Vicente Villardon

Examples

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