000			02796nam a22004098i 4500
001			CR9781108644181
003			UkCbUP
005			20200124160155.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			190123s2019||||enk o ||1 0|eng|d
020			_a9781108644181 (ebook)
020			_z9781108494205 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA278.55 _b.M63 2019
082	0	0	_a519.5/3 _223
100	1		_aBouveyron, Charles, _d1979- _eauthor.
245	1	0	_aModel-based clustering and classification for data science : _bwith applications in R / _cCharles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery.
264		1	_aCambridge : _bCambridge University Press, _c2019.
300			_a1 online resource (xvii, 427 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aCambridge series in statistical and probabilistic mathematics ; _v50
500			_aTitle from publisher's bibliographic system (viewed on 19 Jun 2019).
520			_aCluster 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
650		0	_aCluster analysis.
650		0	_aMathematical statistics.
650		0	_aStatistics _xClassification.
650		0	_aR (Computer program language)
700	1		_aCeleux, Gilles, _eauthor.
700	1		_aMurphy, T. Brendan, _d1972- _eauthor.
700	1		_aRaftery, Adrian E., _eauthor.
776	0	8	_iPrint version: _z9781108494205
830		0	_aCambridge series in statistical and probabilistic mathematics ; _v50.
856	4	0	_uhttps://doi.org/10.1017/9781108644181
999			_c514387 _d514385