Adrian Raftery, Boeing International Professor of Statistics and Sociology at the University of Washington, has published a new book on Model-Based Clustering and Classification for Data Science, with Cambridge University Press, co-authored with Charles Bouveyron, Gilles Celeux and Brendan Murphy.
This book frames cluster analysis and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to the central questions. It builds the basic ideas in an accessible but rigorous way, with extensive data examples and R code; describes modern approaches to high-dimensional data and networks; and explains such recent advances 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. Written for advanced undergraduates in data science, as well as researchers and practitioners, it assumes basic knowledge of multivariate calculus, linear algebra, probability and statistics.
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