Model-based clustering and classification for data science: with applications in R
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 observat...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Weitere beteiligte Personen: | , , |
| Format: | E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2019
|
| Schriftenreihe: | Cambridge series in statistical and probabilistic mathematics
50 |
| Links: | https://doi.org/10.1017/9781108644181 |
| Zusammenfassung: | 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 |
| Umfang: | 1 Online-Ressource (xvii, 427 Seiten) |
| ISBN: | 9781108644181 |
Internformat
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| spelling | Bouveyron, Charles 1979- Model-based clustering and classification for data science with applications in R Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery Cambridge Cambridge University Press 2019 1 Online-Ressource (xvii, 427 Seiten) txt c cr Cambridge series in statistical and probabilistic mathematics 50 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 Celeux, Gilles Murphy, T. Brendan 1972- Raftery, Adrian E. Erscheint auch als Druck-Ausgabe 9781108494205 |
| spellingShingle | Bouveyron, Charles 1979- Model-based clustering and classification for data science with applications in R |
| title | Model-based clustering and classification for data science with applications in R |
| title_auth | Model-based clustering and classification for data science with applications in R |
| title_exact_search | Model-based clustering and classification for data science with applications in R |
| title_full | Model-based clustering and classification for data science with applications in R Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery |
| title_fullStr | Model-based clustering and classification for data science with applications in R Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery |
| title_full_unstemmed | Model-based clustering and classification for data science with applications in R Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery |
| title_short | Model-based clustering and classification for data science |
| title_sort | model based clustering and classification for data science with applications in r |
| title_sub | with applications in R |
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