ISBN: 978-1032234953
eBook ISBN: 978-1003277965


List of Figures
List of Tables
List of Examples
1. Introduction
2. Finite Mixture Models
3. Model-based Clustering
4. Mixture-based Classification
5. Model-based Density Estimation
6. Visualizing Gaussian Mixture Models
7. Miscellanea

This is the website accompanying the book Model-Based Clustering, Classification and Density Estimation Using mclust in R by Luca Scrucca, Chris Fraley, T. Brendan Murphy, and Adrian E. Raftery, published by Chapman & Hall/CRC Press on 2023.

Model-based clustering and classification methods provide a systematic statistical approach to clustering, classification, and density estimation via mixture modeling. This model-based framework allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. The mclust package for the statistical environment R is a widely adopted platform implementing these model-based strategies. The package includes both summary and visual functionality, complementing procedures for estimating and choosing models.

R scripts

R source code for all the chapters can be downloaded here  


None so far.

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The book is part of the Chapman & Hall/CRC The R Series and is available for pre-order on specialized bookshops and e-commerce websites.

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