Exploratory Multivariate Analysis by Example Using R

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missMDA: A Package for Handling Missing Values in Multivariate Data Analysis (2016, background): “More details about how to interpret the results of a PCA can be found in many books such as in Husson, Lê, and Pagès (2010).”
Bank Income Smoothing, Ownership Concentration and the Regulatory Environment (2014, positive): “We then use a more sophisticated clustering approach based on hierarchical agglomerative clustering (HAC) to account more accurately for several dimensions of concentration/dispersion in banks’ownership structures (following Husson et al. 2010, Husson et al. 2011; see Appendix for details).”
Multiple factor analysis: principal component analysis for multitable and multiblock data sets (2013, background): “…for understanding the statistical structure of each block, even though the relative positions of the factor scores and the loadings are not directly interpretable because only the projections of observations on the loading vectors can be meaningfully interpreted in a biplot (cf., Refs 87,88).”
Pollutants bioavailability and toxicological risk from microplastics to marine mussels. (2015, methodology): “component analysis (PCA) was combined to hierarchical clustering of the PCA patterns which 243 visualize the relationships among the different treatments and organize the samples into groups of 244 homogeneous observations (Husson et al., 2010).”