Possibilistic ordination-based analysis of an imperfect database

An approach that aims to reveal and to explain the pattern of information potentially present in a dataset consisting of n objects by ordering them using the possibility-based Robinsonian similarity matrix is proposed. The similarity is estimated between objects containing imperfect and heterogeneously-assigned data. A graph-based model is proposed to visualize these patterns. This method is applied to a medical database. Without any a priori medical knowledge and without knowing the key attributes of the pathologies, the objects have been ranked according to their corresponding classes.