Mixture Gaussian Clustering
Model based clustering using mixtures of gaussian distributions.
mgc(x, NG = 2, init = "km", RemoveOutliers = FALSE, ConfidOutliers = 0.995, tolerance = 1e-07, maxiter = 100, show = TRUE, ...)
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? |
... |
Any other parameter that can affect k-means if that is the initial configuration. |
A basic algorithm for clustering with mixtures of gaussians with no restrictions on the covariance matrices.
Clusters.
Jose Luis Vicente-Villardon
X=as.matrix(iris[,1:4]) mod1=mgc(X,NG=3) plot(iris[,1:4], col=mod1$Classification) table(iris[,5],mod1$Classification)
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