# Robust Consensus Clustering and its Applications for Advertising Forecasting

arXiv: https://arxiv.org/abs/2301.00717 · HTML: https://mlsanity.com/paper/2301.00717/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 41 references that are in the map.
- First posted on arXiv 27 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Robust Ensemble Clustering Using Probability Trajectories](https://mlsanity.com/paper/1606.01160/) (2016)
- [Distributed Low-rank Subspace Segmentation](https://mlsanity.com/paper/1304.5583/) (2013)

## Abstract (arXiv)

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.