Practical Guide To Principal Component Methods ... 🔥

The book categorizes methods based on the types of data you are analyzing:

: Hierarchical Clustering on Principal Components (HCPC), which combines dimensionality reduction with clustering techniques. Who Should Read It Practical Guide To Principal Component Methods ...

: Principal Component Analysis (PCA) for quantitative variables. The book categorizes methods based on the types

: The book heavily utilizes the author's own factoextra R package , which creates elegant, ggplot2 -based graphs to help interpret results. : Those who need to analyze large multivariate

: Those who need to analyze large multivariate datasets for research or business but prefer practical implementation over theoretical derivation.

: Factor Analysis of Mixed Data (FAMD) and Multiple Factor Analysis (MFA) for datasets with both continuous and categorical variables.

: Specifically those looking to move beyond "old-school" base R graphics to more modern, publication-ready visualizations. Practical Guide To Principal Component Methods in R