Finding prototypes through a two-step fuzzy approach

Usually, the aim of cluster analysis is to build prototypes, i.e., typologies of units that present similar characteristics. In this paper, an alternative approach based on consensus clustering between two different clustering methods is proposed to obtain homogeneous prototypes. The clustering methods used are fuzzy c-means (that minimizes the objective function with respect to centers of the groups) and archetypal analysis (that minimizes the objective function with respect to extremes of the groups). The consensus clustering is used to assess the correspondence between the clustering solutions obtained and to find the prototypes as a compromise between the two clustering methods.