Model-Based Clustering and Classification for Data Science: With Applications in R (Cambridge Series in Statistical and Probabilistic Mathematics, Series Number 50)
by Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery
An authoritative resource on model-based clustering and classification methods, offering practical R implementations for data science applications and statistical analysis.
About This Book
This book presents model-based approaches to clustering and classification for data science applications.
It covers statistical foundations and probabilistic mathematics underlying modern clustering techniques.
Readers will find detailed explanations of algorithms and their implementation using the R programming language.
The text is part of the Cambridge Series in Statistical and Probabilistic Mathematics.
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