Gaussian Mixture Modelling for Model-Based Clustering, Classification, and Density Estimation
Gaussian finite mixture models estimated via EM algorithm for model-based clustering, classification, and density estimation, including Bayesian regularization and dimension reduction.
For a quick introduction to mclust see the vignette A quick tour of mclust.
See also:
Mclust for clustering;
MclustDA for supervised classification;
MclustSSC for semi-supervised classification;
densityMclust for density estimation.
Chris Fraley, Adrian Raftery and Luca Scrucca.
Maintainer: Luca Scrucca luca.scrucca@unipg.it
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. 289-317.
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.
# 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.