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

<p>Model-based clustering and classification methods provide a systematic statistical approach to clustering classification and density estimation via mixture modeling. The 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 <b>mclust</b> 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.</p><p><b>Key features of the book:</b></p><ul> <li>An introduction to the model-based approach and the <b>mclust</b> R package</li> <li>A detailed description of <strong>mclust</strong> and the underlying modeling strategies</li> <li>An extensive set of examples color plots and figures along with the R code for reproducing them</li> <li>Supported by a companion website including the R code to reproduce the examples and figures presented in the book errata and other supplementary material</li> </ul><p><strong>Model-Based Clustering Classification and Density Estimation Using mclust in R </strong>is accessible to quantitatively trained students and researchers with a basic understanding of statistical methods including inference and computing. In addition to serving as a reference manual for <b>mclust</b> the book will be particularly useful to those wishing to employ these model-based techniques in research or applications in statistics data science clinical research social science and many other disciplines.</p>