Model-based clustering and classification for data science[electronic resource] :with applications in R

  • 其他作者: Bouveyron, Charles,
  • 出版: Cambridge : Cambridge University Press 2019.
  • 稽核項: xvii, 427 p. :ill., digital ;26 cm.
  • 叢書名: Cambridge series in statistical and probabilistic mathematics ;50
  • 標題: Statistics , R (Computer program language) , Statistics Classification. , Classification. , Mathematical statistics. , Cluster analysis.
  • ISBN: 110849420X , 9781108494205
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  • 附註: Title from publisher's bibliographic system (viewed on 19 Jun 2019).
  • 摘要: 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
  • 電子資源: https://dbs.tnua.edu.tw/login?url=https://doi.org/10.1017/9781108644181
  • 系統號: 005338411
  • 資料類型: 電子書
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  • 引用網址: 複製連結