R: Gaussian Mixture Modelling for Model-Based Clustering,...

mclust-package {mclust} | R Documentation

## Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation

### Description

Finite Gaussian mixture modelling 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.

### Author(s)

Chris Fraley, Adrian Raftery and Luca Scrucca.

Maintainer: Luca Scrucca luca.scrucca@unipg.it

### References

Scrucca L., Fop M., Murphy T. B. and Raftery A. E. (2016) mclust 5: clustering, classification and density estimation using Gaussian finite mixture models, The R Journal, 8/1, pp. 205-233.

Fraley C. and Raftery A. E. (2002) Model-based clustering, discriminant analysis and density estimation, Journal of the American Statistical Association, 97/458, pp. 611-631.

Fraley C., Raftery A. E., Murphy T. B. and Scrucca L. (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.

### 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 5.4.2 Index]