Model-based Clustering and Classification for Data Science

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.

Author:

Charles Bouveyron, Gilles Celeux, T. Brendan Murphy, Adrian E. Raftery

Publisher:

Cambridge University Press

Publication Date:

Jul 25, 2019

Number of pages:

446 pages

Language:

English

Binding:

Hardcover/Business & Economics

ISBN-10:

110849420X

ISBN-13:

9781108494205