An approach for clustering objects containing imperfect and heterogeneously-assigned data is proposed. This approach depends mainly on possibility theory to estimate the similarity between objects, and on belief theory and multidimensional scaling methods to assign relevant classes to them. This unsupervised clustering method has been applied to a medical database and robust results have been obtained with the absence of any a priori medical knowledge, and without knowing the key attributes of the concerned pathologies.