Principal component methods - hierarchical clustering - partitional clustering: why would we need to choose for visualizing data?

Abstract

This paper combines three exploratory data analysis methods, principal component methods, hierarchical clustering and partitioning, to enrich the description of the data. Principal component methods are used as preprocessing step for the clustering in order to denoise the data, transform categorical data in continuous ones or balanced groups of variables. The principal component representation is also used to visualize the hierarchical tree and/or the partition in a 3D-map which allows to better understand the data. The proposed methodology is available in the HCPC (Hierarchical Clustering on Principal Components) function of the FactoMineR package.