  Consensus clustering aggregates partitions in order to find a better fit by
reconciling clustering results from different sources/executions. In practice,
there exist noise and outliers in clustering task, which, however, may
significantly degrade the performance. To address this issue, we propose a
novel algorithm -- robust consensus clustering that can find common ground
truth among experts' opinions, which tends to be minimally affected by the bias
caused by the outliers. In particular, we formalize the robust consensus
clustering problem as a constraint optimization problem, and then derive an
effective algorithm upon alternating direction method of multipliers (ADMM)
with rigorous convergence guarantee. Our method outperforms the baselines on
benchmarks. We apply the proposed method to the real-world advertising campaign
segmentation and forecasting tasks using the proposed consensus clustering
results based on the similarity computed via Kolmogorov-Smirnov Statistics. The
accurate clustering result is helpful for building the advertiser profiles so
as to perform the forecasting.
