Title: Relaxed Oracles for Semi-Supervised Clustering,
Abstract: Pairwise "same-cluster" queries are one of the most widely used forms of
supervision in semi-supervised clustering. However, it is impractical to ask
human oracles to answer every query correctly. In this paper, we study the
influence of allowing "not-sure" answers from a weak oracle and propose an
effective algorithm to handle such uncertainties in query responses. Two
realistic weak oracle models are considered where ambiguity in answering
depends on the distance between two points. We show that a small query
complexity is adequate for effective clustering with high probability by
providing better pairs to the weak oracle. Experimental results on synthetic
and real data show the effectiveness of our approach in overcoming supervision
uncertainties and yielding high quality clusters.