Robust Consensus Clustering and its Applications for Advertising Forecasting
This work addresses the issue of noisy clustering for applications like advertising forecasting, but it appears incremental as it builds on existing consensus clustering methods with robustness improvements.
The paper tackles the problem of noise and outliers degrading consensus clustering performance by proposing a robust algorithm that formalizes the problem as a constraint optimization and solves it with ADMM, showing it outperforms baselines on benchmarks and applying it to advertising campaign segmentation and forecasting.
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.