Robust Consensus Clustering and its Applications for Advertising Forecasting

TL;DR

This paper introduces a robust consensus clustering algorithm designed to handle noise and outliers, improving clustering accuracy for applications like advertising forecasting by aggregating multiple expert opinions.

Contribution

The paper presents a novel robust consensus clustering method formulated as a constrained optimization problem, with an ADMM-based algorithm that guarantees convergence and outperforms existing baselines.

Findings

The proposed method outperforms baseline algorithms on benchmark datasets.

Application to advertising segmentation improves forecasting accuracy.

Robust clustering effectively handles noise and outliers in real-world data.

Abstract

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.