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Model-based clustering and classification for data science / Charles Bouveyron (2019)

Public ISBD Titre : Model-based clustering and classification for data science : with applications in R Type de document : texte imprimé Auteurs : Charles Bouveyron (1979-....), Auteur ; Gilles Celeux, Auteur ; Thomas Brendan Murphy, Auteur ; Adrian E. Raftery Editeur : Cambridge : Cambridge University Press Année de publication : 2019 Collection : Cambridge series in statistical and probabilistic mathematics, ISSN 2633-0199 Importance : 1 vol. (xvii-427 p.) Présentation : ill., couv. ill. en coul. ; 26 cm Format : 26 cm ISBN/ISSN/EAN : 978-1-108-49420-5 Note générale : PPN 240418786 Langues : Anglais (eng) Tags : R (logiciel)  Classification automatique (statistique)  Analyse des données  Modèles mathématiques  R (Computer program language)  Cluster analysis  Mathematical statistics  Statistics -- Classification Index. décimale : 519.53 Statistiques descriptives, analyse multivariée, analyse de la variance et de la covariance Résumé : 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. Note de contenu : Bibliogr. p. [386]-414. Index Model-based clustering and classification for data science : with applications in R [texte imprimé] / Charles Bouveyron (1979-....), Auteur ; Gilles Celeux, Auteur ; Thomas Brendan Murphy, Auteur ; Adrian E. Raftery . - Cambridge : Cambridge University Press, 2019 . - 1 vol. (xvii-427 p.) : ill., couv. ill. en coul. ; 26 cm ; 26 cm. - (Cambridge series in statistical and probabilistic mathematics, ISSN 2633-0199) . ISBN : 978-1-108-49420-5 PPN 240418786 Langues : Anglais (eng) Tags : R (logiciel)  Classification automatique (statistique)  Analyse des données  Modèles mathématiques  R (Computer program language)  Cluster analysis  Mathematical statistics  Statistics -- Classification Index. décimale : 519.53 Statistiques descriptives, analyse multivariée, analyse de la variance et de la covariance Résumé : 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. Note de contenu : Bibliogr. p. [386]-414. Index

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Code-barres	Cote	Support	Localisation	Section	Disponibilité	Nom du donateur
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