Model-Based Clustering and Classification for Data Science: With Applications in R

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ID: 288151
2019
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Abstract
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.
Reference Key
persistent_1761420396_68fd246c99646 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Adrian E. Raftery
Journal ADVANCES IN ARCHAEOLOGICAL PRACTICE
Year 2019
DOI
10.1017/9781108644181
URL
https://www.cambridge.org/core/books/modelbased-clustering-and-classification-for-data-science/E92503A3984DC4F1F2006382D0E3A2D7
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