... — Practical Guide To Principal Component Methods

: Principal Component Analysis (PCA) for quantitative variables.

The by Alboukadel Kassambara is widely considered an excellent resource for those who want to apply multivariate analysis without getting bogged down in heavy mathematical proofs. Why It Is Highly Rated Practical Guide To Principal Component Methods ...

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

: It simplifies complex statistical concepts into digestible pieces, focusing on intuitive explanations rather than advanced theory. Practical Guide To Principal Component Methods ...

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

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