Model-based clustering and classification for data science : with applications in R / Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery
Material type:![Text](/opac-tmpl/lib/famfamfam/BK.png)
- 9781108494205
- 519.53 BOU-C 23
- QA278.55 .M63 2019
Item type | Current library | Collection | Shelving location | Call number | Status | Notes | Date due | Barcode | Item holds |
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BITS Pilani Hyderabad | 510 | General Stack (For lending) | 519.53 BOU-C (Browse shelf(Opens below)) | Checked out | Project Book : Dr. Sayan Ghosh. | 17/10/2024 | 46473 |
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519.52 CHA-A Essentials of survey sampling / | 519.52 LOH-S Sampling : design and analysis / | 519.52 ORC-L Conducting a survey : | 519.53 BOU-C Model-based clustering and classification for data science : with applications in R / | 519.53 HE-Y Multiple imputation of missing data in practice : basic theory and analysis strategies / | 519.53 RIG-J Handbook for applied modeling : non-gaussian and correlated data / | 519.535 AND-T Introduction to multivariate statistical analysis by |
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.
Includes bibliographical references and index.
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