Although there are several good books on unsupervised machine learning, we felt that many of them are too theoretical. This book provides practical guide to cluster analysis, elegant visualization and interpretation.
This book provides a solid practical guidance to summarize, visualize and interpret the most important information in a large multivariate data sets, using principal component methods in R.
An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.
Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R, Second Edition focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for... [Read more]