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. In this article, we propose a general framework for the development of external evaluation measures for soft clustering. Our proposal is based on the interpretation of soft clustering as representing uncertain information about an underlying, unknown hard clustering. We present a general construction, based on optimal transport theory, by which any evaluation measure can be naturally extended to soft clustering. The proposed "transport-based measure" provides an objective, interval-valued comparison index that represents the range of compatibility between two soft clusterings. We study the metric and complexity properties of the proposed approach, as well as its relationship with other existing proposals. We also propose approximation and bounding algorithms that make the approach practical for large datasets. Finally, we illustrate the application of the proposed method through two computational experiments. [ABSTRACT FROM AUTHOR]