Crowdsourced Correlation Clustering with Relative Distance Comparisons

Crowdsourced, or human computation based clustering algorithms usually rely on relative distance comparisons, as these are easier to elicit from human workers than absolute distance information. We build upon existing work on correlation clustering, a well-known non-parametric approach to clustering, and present a novel clustering algorithm for human computation. We first define a novel variant of correlation clustering that is based on relative distance comparisons, and briefly outline an approximation algorithm for this problem. As a second contribution, we propose a more practical algorithm, which we empirically compare against existing methods from literature. Experiments with synthetic data suggest that our approach can outperform more complex methods. Also, our method efficiently finds good and intuitive clusterings from real relative distance comparison data.