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Model-based clustering and classification for data science : = with applications in R /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Model-based clustering and classification for data science :/ Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery.
Reminder of title:	with applications in R /
Author:	Bouveyron, Charles,
other author:	Celeux, Gilles,
Description:	1 online resource (xvii, 427 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 19 Jun 2019).
Subject:	Cluster analysis. -
Online resource:	https://doi.org/10.1017/9781108644181
ISBN:	9781108644181 (ebook)

Model-based clustering and classification for data science : = with applications in R /
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. - 1 online resource (xvii, 427 pages) :digital, PDF file(s). - Cambridge series in statistical and probabilistic mathematics ;50. - Cambridge series in statistical and probabilistic mathematics ;53..

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

ISBN: 9781108644181 (ebook)Subjects--Topical Terms:

562949
Cluster analysis.

LC Class. No.: QA278.55 / .M63 2019

Dewey Class. No.: 519.5/3

Model-based clustering and classification for data science : = with applications in R /
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500 $a Title from publisher's bibliographic system (viewed on 19 Jun 2019).
520 $a 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
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