Correlation Clustering with Active Learning of Pairwise Similarities

Correlation clustering is a well-known unsupervised learning setting that deals with positive and negative pairwise similarities. In this paper, we study the case where the pairwise similarities are not given in advance and must be queried in a cost-efficient way. Thereby, we develop a generic active learning framework for this task that benefits from several advantages, e.g., flexibility in the type of feedback that a user/annotator can provide, adaptation to any correlation clustering algorithm and query strategy, and robustness to …

active learning advance case clustering correlation cost cs.lg deals framework negative paper positive stat.ml study unsupervised unsupervised learning