Model-Based Clustering and Classification for Data Science

'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