Fuzzy and Model Based Clustering Methods: Can We Fruitfully Compare Them?

During the last years, fuzzy and model-based approaches to clustering have received a great deal of attention and have been increasingly used in several empirical contexts. Even if they are very different from a theoretical point of view, they are similar in practice. In fact, model-based clustering gives posterior probabilities of component, treated as cluster, membership. Fuzzy clustering assigns observations to clusters through fuzzy membership degrees, while no probabilistic assumption is made to represent the clusters. The aim of this work is to compare the performance of some clustering methods belonging to the two approaches, in terms of recovering the true clusters, in a large scale simulation study.