BordaConsensus: a new consensus function for soft cluster ensembles

Consensus clustering is the task of deriving a single labeling by applying a consensus function on a cluster ensemble. This work introduces BordaConsensus, a new consensus function for soft cluster ensembles based on the Borda voting scheme. In contrast to classic, hard consensus functions that operate on labelings, our proposal considers cluster membership information, thus being able to tackle multiclass clustering problems. Initial small scale experiments reveal that, compared to state-of-the-art consensus functions, BordaConsensus constitutes a good performance vs. complexity trade-off.