Cautious relational clustering: A thresholding approach

Abstract

We propose in this article a new relational clustering method that can return a partial answer (i.e., a set of clusterings) in some cases. Starting from relational or similarity data, we determine a partial equivalence relation defined on the set of objects (two objects are linked if they belong to the same cluster): the key idea is to allow the method to abstain on some pairwise links because they cannot be determined with enough certainty from the data. This cautious equivalence relation represents a set of possible hard clusterings which can be obtained by completing the partial relation. This formalization makes it possible to easily detect ambiguous links and to identify subsets of objects with uncertain relationship. We illustrate the potential interest of our approach as a tool for exploratory data analysis of synthetic and real data sets.