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

This book provides an introduction to the model-based approach to clustering data and the mclust R package. It describes the underlying modeling strategies, offers extensive examples, color plots, and figures, and includes R code for reproducing the examples and figures. It also supports a companion website with additional material.

Format: Paperback / softback
Length: 242 pages
Publication date: 20 April 2023
Publisher: Taylor & Francis Ltd

A comprehensive guide to the model-based approach and the mclust R package

This book provides a thorough introduction to the model-based approach and the mclust R package, a powerful tool for clustering data. It offers a detailed description of mclust and the underlying modeling strategies employed in its implementation. Additionally, it includes an extensive set of examples, color plots, and figures, accompanied by the R code necessary to reproduce them. Furthermore, the book is supported by a companion website that offers additional resources, such as the R code to reproduce the examples and figures presented in the book, errata, and other supplementary material.

The model-based approach, as advocated in this book, is a powerful tool for analyzing and interpreting data. It involves constructing mathematical models that capture the underlying structure and relationships within the data. By leveraging the capabilities of mclust, researchers can efficiently cluster data, identify meaningful patterns, and make informed decisions based on the results.

The book begins by providing an overview of the model-based approach and its advantages over traditional clustering methods. It then delves into the details of mclust, explaining its features, functionalities, and syntax. The subsequent chapters cover various modeling strategies, including hierarchical clustering, k-means clustering, and Gaussian mixture models, and demonstrate their application in real-world data sets.

Throughout the book, practical examples are used to illustrate the concepts and techniques discussed. Each example includes step-by-step instructions, R code, and output, allowing readers to replicate the analysis and obtain similar results. The accompanying figures and color plots enhance the understanding of the topics and provide visual representations of the data.

In addition to its theoretical aspects, the book emphasizes the importance of statistical inference and hypothesis testing in data clustering. It provides guidance on how to assess the quality of the clusters, evaluate the performance of different modeling strategies, and make meaningful conclusions based on the analysis.

Whether you are a novice in data analysis or a seasoned researcher, this book offers valuable insights and practical techniques for using the model-based approach and mclust R package. It will help you unlock the hidden patterns and insights in your data and make informed decisions based on empirical evidence.

Weight: 446g
Dimension: 154 x 234 x 18 (mm)
ISBN-13: 9781032234953