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Model-Based Clustering and Classification for Data Science: With Applications in R
— —
Charles Bouveyron Gilles Celeux
Model-Based Clustering and Classification for Data Science: With Applications in R by Charles Bouveyron at Abbey's Bookshop,

Model-Based Clustering and Classification for Data Science: With Applications in R

Charles Bouveyron Gilles Celeux T. Brendan Murphy (University College Dublin) Adrian E. Raftery (University of Washington)


9781108494205

Cambridge University Press


Social research & statistics;
Probability & statistics;
Data capture & analysis;
Data mining;
Machine learning


Hardback

446 pages

$109.95
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Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many clusters are there? Which method should I use? How should I handle outliers? Classification assigns new observations to groups given previously classified observations, and also has open questions about parameter tuning, robustness and uncertainty assessment. 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.

By:   Charles Bouveyron, Gilles Celeux, T. Brendan Murphy (University College Dublin), Adrian E. Raftery (University of Washington)
Imprint:   Cambridge University Press
Country of Publication:   United Kingdom
Dimensions:   Height: 260mm,  Width: 185mm,  Spine: 25mm
Weight:   1.100kg
ISBN:   9781108494205
ISBN 10:   110849420X
Series:   Cambridge Series in Statistical and Probabilistic Mathematics
Pages:   446
Publication Date:   August 2019
Audience:   Professional and scholarly ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active

Charles Bouveyron is Full Professor of Statistics at Universite Cote 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.


'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

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