Model based clustering and classification for data science : with applications in R 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