Condorcet’s jury theorem for consensus clustering

Abstract

Condorcet’s Jury Theorem has been invoked for ensemble classifiers to indicate that the combination of many classifiers can have better predictive performance than a single classifier. Such a theoretical underpinning is unknown for consensus clustering. This article extends Condorcet’s Jury Theorem to the mean partition approach under the additional assumptions that a unique but unknown ground-truth partition exists and sample partitions are drawn from a sufficiently small ball containing the ground-truth.