- Publisher: Cambridge University Press
- Online publication date: June 2019
- Print publication year: 2019
- Online ISBN: 9781108644181
- DOI: https://doi.org/10.1017/9781108644181
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.
John S. Ahlquist - University of California, San Diego
Bettina Grün - Johannes Kepler Universität Linz, Austria
Naisyin Wang - University of Michigan
Rob McCulloch - Arizona State University
Antony Unwin Source: International Statistical Review
Zdenek Hlavka Source: MathSciNet
Li-Pang Chen Source: Biometrical Journal
C. M. Foley Source: Quarterly Review of Biology
Hans-Jürgen Schmidt Source: zbMATH
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