Large-scale Robust Enhanced Ensemble Clustering via Outlier Decoupling

Ensemble clustering aims to derive a consensus partition from multiple base clustering results. Anchor-based methods construct compact similarity representations via anchors, substantially improving computational efficiency. However, when outliers contaminate the data, reconstructing the base clustering results often yields biased anchors. These biased anchors degrade the quality of the anchor similarity matrix and lead to a decline in clustering accuracy. To address this issue, we propose a novel method called large-scale robust enhanced ensemble clustering via outlier decoupling (RANGE). Specifically, RANGE first converts the base clustering results into an initial bipartite graph. To enhance the reliability of this bipartite graph, RANGE designs a high-order fuzzy enhancement strategy (HFES) specifically for initial bipartite graphs. Next, RANGE introduces an anchor matrix to improve the computational efficiency of the bipartite graph reconstruction process. To improve robustness, RANGE decomposes the anchor similarity matrix into a clean component and a residual outlier component in the anchor space. A global cross-correlation penalty is imposed to suppress leakage between the two components, while a row-wise ℓ 2,1 -norm on the residual part confines contamination to a small number of residual anchor directions. Moreover, by applying outlier detectors to the decoupled outlier structure, RANGE can be extended to the outlier detection task. Consequently, RANGE forms a cross-task general framework. Extensive experiments indicate that RANGE delivers superior performance in both clustering validity and outlier detection.