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9781108494205

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'Bouveyron, Celeux, Murphy, and Raftery pioneered the theory, computation, and application of modern model-based clustering and discriminant analysis. Here they have produced an exhaustive yet accessible text, covering both the field's state of the art as well as intellectual development. The authors develop a unified vision of cluster analysis, rooted in the theory and computation of mixture models. Embedded R code points the way for applied readers, while graphical displays serve to develop intuition about both model construction and the critical but often-neglected estimation process. Building on a series of running examples, the authors gradually and methodically extend their core insights into a variety of exciting data structures, including networks and functional data. This text will serve as a backbone for graduate study as well as an important reference for applied data scientists interested in working with cutting edge tools in semi- and unsupervised machine learning.' John S. Ahlquist, University of California, San Diego 'This book, written by authoritative experts in the field, gives a comprehensive and thorough introduction into model-based clustering and classification. The authors do not only explain the statistical theory and methods, but also provide hands-on applications illustrating the use with the open-source statistical software R. Then, the book also covers recent advances made for specific data structures (e.g., network data) or modeling strategies (e.g., variable selection techniques) making it a fantastic resource for an up-to-date overview on the state of the field today.' Bettina Grun, Johannes Kepler Universitat Linz, Austria `Bouveyron, Celeux, Murphy, and Raftery pioneered the theory, computation, and application of modern model-based clustering and discriminant analysis. Here they have produced an exhaustive yet accessible text, covering both the field's state of the art as well as intellectual development. The authors develop a unified vision of cluster analysis, rooted in the theory and computation of mixture models. Embedded R code points the way for applied readers, while graphical displays serve to develop intuition about both model construction and the critical but often-neglected estimation process. Building on a series of running examples, the authors gradually and methodically extend their core insights into a variety of exciting data structures, including networks and functional data. This text will serve as a backbone for graduate study as well as an important reference for applied data scientists interested in working with cutting edge tools in semi- and unsupervised machine learning.' John S. Ahlquist, University of California, San Diego 'This book, written by authoritative experts in the field, gives a comprehensive and thorough introduction into model-based clustering and classification. The authors do not only explain the statistical theory and methods, but also provide hands-on applications illustrating the use with the open-source statistical software R. Then, the book also covers recent advances made for specific data structures (e.g., network data) or modeling strategies (e.g., variable selection techniques) making it a fantastic resource for an up-to-date overview on the state of the field today.' Bettina Grun, Johannes Kepler Universitat Linz, Austria

Author Information

Charles Bouveyron is Full Professor of Statistics at Université Côte d'Azur and the Chair of Excellence in Data Science at Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt. He has published extensively on model-based clustering, particularly for networks and high-dimensional data. Gilles Celeux is Director of Research Emeritus at Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt. He is one of the founding researchers in model-based clustering, having published extensively in the area for thrity-five years. T. Brendan Murphy is Full Professor in the School of Mathematics and Statistics at University College Dublin. His research interests include model-based clustering, classification, network modeling and latent variable modeling. Adrian E. Raftery is the Boeing International Professor of Statistics and Sociology at the University of Washington. He is one of the founding researchers in model-based clustering, having published in the area since 1984.

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