Название: Model-Based Clustering and Classification for Data Science: With Applications in R Автор: Charles Bouveyron, Gilles Celeux, T. Brendan Murphy
Издательство: Cambridge University Press
Год: 2019
Страниц: 447
Язык: английский
Формат: pdf (true)
Размер: 61.0 MB
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.
The book is supported by extensive examples on data, with 72 listings of code mobilizing more than 30 software packages, that can be run by the reader. The chosen language for codes is the R software, which is one of the most popular languages for data science. It is an excellent tool for data science since the most recent statistical learning techniques are provided on the R platform (named CRAN). Using R is probably the best way to be directly connected to current research in statistics and data science through the packages provided by researchers.
Скачать Model-Based Clustering and Classification for Data Science: With Applications in R