A general framework for evaluating and comparing soft clusterings

•We propose a novel framework of measures to evaluate soft clustering.•Our methods allow to generalize every hard clustering measure using optimal transport.•We study the metric and computational properties of the proposed methods.•We also propose computationally efficient approximation and sampling algorithms.•Our methods allow better quantification of uncertainty than previous proposals.