Model-Based Clustering and Classification for Data Science: With Applications in R

Cluster analysis and classification are statistical models that automate the discovery of groups in data and assign new observations to groups. This book provides principled estimation, testing, and prediction methods, as well as sound answers to central questions, with extensive data examples and R code. It is written for advanced undergraduates in data science, researchers, and practitioners, and assumes basic knowledge of multivariate calculus, linear algebra, probability, and statistics.

\n Format: Hardback
\n Length: 446 pages
\n Publication date: 25 July 2019
\n Publisher: Cambridge University Press
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Cluster analysis is a powerful tool that automatically identifies groups within data. While many methods have been employed in the past, they often rely on heuristics and leave important questions unanswered, such as determining the optimal number of clusters and selecting the most suitable method. Classification, on the other hand, involves assigning new observations to predefined groups based on previously classified data. It also presents challenges related to parameter tuning, robustness, and uncertainty assessment.

This book aims to provide a comprehensive framework for understanding cluster analysis and classification in terms of statistical models. By doing so, it offers principled estimation, testing, and prediction methods, as well as sound answers to the central questions. The book begins by building the fundamental concepts in an accessible yet rigorous manner, utilizing extensive data examples and R code. It then explores modern approaches to high-dimensional data and networks, covering topics such as Bayesian regularization, non-Gaussian model-based clustering, cluster merging, variable selection, semi-supervised and robust classification, clustering of functional data, text and images, and co-clustering.

Designed for advanced undergraduates in data science, as well as researchers and practitioners, this book assumes a basic understanding of multivariate calculus, linear algebra, probability, and statistics. It serves as a valuable resource for anyone seeking to delve deeper into the field of cluster analysis and classification, providing a comprehensive and up-to-date overview of the latest techniques and applications.

\n Weight: 1112g\n
Dimension: 186 x 260 x 25 (mm)\n
ISBN-13: 9781108494205\n \n