Mixture model-based classification. (English) Zbl 1454.62005

McNicholas, Paul D.

Boca Raton, FL: CRC Press (ISBN 978-1-4822-2566-2/hbk; 978-1-4822-2567-9/ebook). xxiv, 212 p. (2017).

Publisher’s description: This book is the first monograph devoted to mixture model-based approaches to clustering and classification. This is both a book for established researchers and newcomers to the field. A history of mixture models as a tool for classification is provided and Gaussian mixtures are considered extensively, including mixtures of factor analyzers and other approaches for high-dimensional data. Non-Gaussian mixtures are considered, from mixtures with components that parameterize skewness and/or concentration, right up to mixtures of multiple scaled distributions. Several other important topics are considered, including mixture approaches for clustering and classification of longitudinal data as well as discussion about how to define a cluster.

MSC:

62-02	Research exposition (monographs, survey articles) pertaining to statistics
62H30	Classification and discrimination; cluster analysis (statistical aspects)
62E10	Characterization and structure theory of statistical distributions
62H25	Factor analysis and principal components; correspondence analysis