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Название: Model-Based Clustering, Classification, and Density Estimation Using mclust in R
Автор: Luca Scrucca, Chris Fraley, T. Brendan Murphy
Издательство: CRC Press
Серия: The R Series
Год: 2023
Страниц: 269
Язык: английский
Формат: pdf (true)
Размер: 28.26 MB
Model-based clustering and classification methods provide a systematic statistical modeling framework for cluster analysis and classification. The model-based approach has gained in popularity because it allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. Mclust is a widely-used software package for the statistical environment R. It provides functionality for model-based clustering, classification, and density estimation, including methods for summarizing and visualizing the estimated models. This book aims at giving a detailed overview of mclust and its features. A description of the modeling underpinning the software is provided, along with examples of its usage. In addition to serving as a reference manual for mclust, the book will be particularly useful to readers who plan to employ these model-based techniques in their research or applications. The companion website for this book contains the R code to reproduce the examples and figures presented in the book, errata and various supplementary material.
Автор: Luca Scrucca, Chris Fraley, T. Brendan Murphy
Издательство: CRC Press
Серия: The R Series
Год: 2023
Страниц: 269
Язык: английский
Формат: pdf (true)
Размер: 28.26 MB
Model-based clustering and classification methods provide a systematic statistical modeling framework for cluster analysis and classification. The model-based approach has gained in popularity because it allows the problems of choosing or developing an appropriate clustering or classification method to be understood within the context of statistical modeling. Mclust is a widely-used software package for the statistical environment R. It provides functionality for model-based clustering, classification, and density estimation, including methods for summarizing and visualizing the estimated models. This book aims at giving a detailed overview of mclust and its features. A description of the modeling underpinning the software is provided, along with examples of its usage. In addition to serving as a reference manual for mclust, the book will be particularly useful to readers who plan to employ these model-based techniques in their research or applications. The companion website for this book contains the R code to reproduce the examples and figures presented in the book, errata and various supplementary material.