Consensus clustering is an alias for ensemble learning when it is applied to clustering methods. In clustering, each base learner assigns a label to each instance, although it is not conditioned on a specific target. Instead, the base learner generates a number of clusters and assigns each instance to a cluster. The label is the cluster itself. As will be demonstrated later, two base learners, produced by the same algorithm, can generate different clusters. Thus, it is not as straightforward to combine their cluster predictions as it is to combine regression or classification predictions.