R: Normal Mixture Modeling for Model-Based Clustering,...

mclust-package {mclust} | R Documentation

## Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation

### Description

Finite Gaussian mixture modeling fitted via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization and dimension reduction.

### Details

For a quick introduction to mclust see the vignette A quick tour of mclust.

### References

C. Fraley, A. E. Raftery, T. B. Murphy and L. Scrucca (2012). mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation. Technical Report No. 597, Department of Statistics, University of Washington.

C. Fraley and A. E. Raftery (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American Statistical Association 97:611:631.

### Author(s)

Chris Fraley, Adrian Raftery and Luca Scrucca.

Maintainer: Luca Scrucca luca@stat.unipg.it

### Examples

    # Clustering
    mod1 = Mclust(iris[,1:4])
    summary(mod1)
    plot(mod1,  what = c("BIC", "classification"))

    # Classification
    data(banknote)
    mod2 = MclustDA(banknote[,2:7], banknote$Status)
    summary(mod2)
    plot(mod2)

    # Density estimation
    mod3 = densityMclust(faithful$waiting)
    summary(mod3)
    plot(mod3, faithful$waiting)

[Package mclust version 5.3 Index]