: Those who need to analyze large multivariate datasets for research or business but prefer practical implementation over theoretical derivation.
: Hierarchical Clustering on Principal Components (HCPC), which combines dimensionality reduction with clustering techniques. Who Should Read It
: It is structured with short, self-contained chapters and "R lab" sections that walk through real-world applications and tested code examples. Core Methods Covered Practical Guide To Principal Component Methods ...
: Principal Component Analysis (PCA) for quantitative variables.
: It simplifies complex statistical concepts into digestible pieces, focusing on intuitive explanations rather than advanced theory. : Those who need to analyze large multivariate
: The book heavily utilizes the author's own factoextra R package , which creates elegant, ggplot2 -based graphs to help interpret results.
: Simple Correspondence Analysis (CA) for two variables and Multiple Correspondence Analysis (MCA) for more than two. Core Methods Covered : Principal Component Analysis (PCA)
: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA) for datasets with both continuous and categorical variables.