Effective Acquisition Functions for Active Correlation Clustering
Linus Aronsson Morteza Haghir Chehreghani
Correlation clustering is a powerful unsupervised learning paradigm that
supports positive and negative similarities. In this paper, we assume the
similarities are not known in advance. Instead, we employ active learning to
iteratively query similarities in a cost-efficient way. In particular, we
develop three effective acquisition functions to be used in this setting. One
is based on the notion of inconsistency (i.e., when similarities violate the
transitive property). The remaining two are based on information-theoretic
quantities, i.e., entropy and information gain.