Model-Based Clustering, Classification, and Density Estimation Using mclust in R

"The book gives an excellent introduction to using the R package mclust for mixture modeling with (multivariate) Gaussian distributions as well as covering the supervised and semi-supervised aspects. A thorough introduction to the theoretic concepts is given, the software implementation described in detail and the application shown on many examples. I particularly enjoyed the in-depth discussion of different visualization methods."
~ Bettina Gruen, WU (Vienna University of Economics and Business), Austria

"Cluster analysis, and its sister subjects of density estimation and mixture-model classification, used to be underserved topics in statistical texts. This magisterial book corrects that imbalance and does so comprehensively."
~ David Banks (Duke University)

"mclust is probably the R-package I use most. This book provides a clear, comprehensive, well-illustrated hands-on introduction to its many features. I particularly like the emphasis on various visualization methods and uncertainty quantification."
~Christian Martin Hennig (University of Bologna)

"The mclust R package has become synonymous with model-based clustering, classification and density estimation, and this book provides an excellent resource for the now millions of users, and future users, of mclust. The book elegantly balances the statistical detail required to have a broad understanding of the methods available in mclust, alongside practical applications of these methods through detailed code and real data examples. The book provides an excellent scaffold to support an mclust user through the concepts and application of model-based clustering, classification and density estimation. The chapter on visualisation in the context of model-based clustering and classification is a unique contribution, collating important topics that to date have received scant attention in this area. This book is essential reading for any practitioner of model-based clustering, classification or density estimation."
~Claire Gormley (University College Dublin)