  We study correlation clustering where the pairwise similarities are not known
in advance. For this purpose, we employ active learning to query pairwise
similarities in a cost-efficient way. We propose a number of effective
information-theoretic acquisition functions based on entropy and information
gain. We extensively investigate the performance of our methods in different
settings and demonstrate their superior performance compared to the
alternatives.
