Model-Based Clustering and Classification for Data Science (Hardcover)
Language: English
Published by Cambridge University Press, Cambridge, 2019
Series: Book 1 of 64 - Cambridge Series in Statistical and Probabilistic Mathematics
- Hardcover
- New
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
AbeBooks seller since June 29, 2022
Condition: New
£ 85.99
Quantity: 1 available
Add to basketItem description from seller
Hardcover. 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. This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…
Seller Inventory # 9781108494205
- Title
- Model-Based Clustering and Classification for Data Science (Hardcover)
- Author
- Charles Bouveyron
- Publisher
- Cambridge University Press, Cambridge
- Publication year
- 2019
- Condition
- new
- Binding
- Hardcover
- Language
- English
- ISBN 10
- 110849420X
- ISBN 13
- 9781108494205
- Series
- Book 1 of 64: Cambridge Series in Statistical and Probabilistic Mathematics
"Synopsis" may belong to another edition of this title.
About the Author
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.
"About the title" may belong to another edition of this title.
CitiRetail
Stevenage, United Kingdom
AbeBooks seller since June 29, 2022
Shipping rates from United Kingdom to U.S.A.
| Item | 7 to 14 business days | 7 to 60 business days |
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