Model-based clustering and classification for data science : with applications in R / Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery.
By: Bouveyron, Charles [author.].
Contributor(s): Celeux, Gilles [author.] | Murphy, T. Brendan [author.] | Raftery, Adrian E [author.].
Series: Cambridge series on statistical and probabilistic mathematics: 50.Publisher: Cambridge : Cambridge University Press, 2019Description: 1 online resource (xvii, 427 pages) : digital, PDF file(s).Content type: text Media type: computer Carrier type: online resourceISBN: 9781108644181 .Subject(s): Cluster analysis | Mathematical statistics | Statistics -- Classification | R (Computer program language)Genre/Form: Electronic booksAdditional physical formats: Print version: : No titleDDC classification: 519.53 Online resources: Click here to access online Summary: 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| Item type | Current location | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|
| Taylor's Library-TU | On order |
Title from publisher's bibliographic system (viewed on 19 Jun 2019).
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