This paper presents a comparative study between a method for fuzzy clustering which retrieves \textit{pure} individual types from data, the fuzzy clustering with proportional membership (FCPM), and an archetypal analysis algorithm based on Furthest-Sum approach (FS-AA). A simulation study comprising 82 data sets is conducted with a proper data generator, FCPM-DG, whose goal is twofold: first, to analyse the ability of archetypal clustering algorithm to recover Archetypes from data of distinct dimensionality; second, to analyse robustness of FCPM and FS-AA algorithms to outliers. The effectiveness of these algorithms are yet compared on clustering 12 diverse benchmark data sets from machine learning. The evaluation conducted with five primer unsupervised validation indices shows the good quality of the clustering solutions.
