Clustering interval-valued proximity data using belief functions

Abstract

The problem of clustering objects based on interval-valued dissimilarities is tackled in the framework of the Dempster–Shafer theory of belief functions. The proposed method assigns to each object a basic belief assignment (or mass function) defined on the set of clusters, in such a way that the belief and the plausibility that any two objects belong to the same cluster reflect, respectively, the observed lower and upper dissimilarity values. Experiments with synthetic and real data sets demonstrate the ability of the method to detect meaningful clusters, even in the presence of imprecise data and outliers.