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Dynamic Anchor-Based One-Step Hypergraph Ensemble Clustering
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DOI：10.1109/tkde.2026.3710223.png)
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Abstract
En 中文
Ensemble clustering aims to derive a more robust consensus result from a set of base clustering results. Recently, anchorbased clustering methods have improved computational efficiency by learning the relationships between anchors and samples, thereby avoiding the expensive computation of pairwise sample similarities. However, these methods typically rely on a static anchor selection strategy and require post-processing to obtain the final clustering assignments. This often leads to inflexible anchors and a low-quality anchor similarity matrix, ultimately degrading clustering accuracy. To address this issue, we propose a novel ensemble clustering method named Dynamic ancho R-based One-step hypE rgrAph ense M ble clustering (DREAM). Specifically, DREAM first transforms the base clustering results into a hypergraph. It then introduces a novel hypergraph enhancement strategy to improve the reliability of the initial hypergraph. Next, DREAM introduces a mapping matrix to filter redundant information in the hypergraph, and reconstructs the hypergraph via matrix factorization to obtain the anchor similarity matrix. Subsequently, DREAM introduces an alignment mechanism that refines the anchor similarity matrix by generating local consensus information. The alignment is achieved by maximizing the element-wise consistency between the anchor similarity matrix and the local consensus information. This yields a high-quality anchor similarity matrix that can be directly projected into the label space, enabling one-step generation of clustering results without any additional post-processing. Extensive experimental results demonstrate the superior performance of the proposed DREAM method
Keywords:
Matrices
Educational institutions
Learning (artificial intelligence)
Optimization
Conferences
Accuracy
Artificial intelligence
Clustering methods
Boats
Labeling
One-step ensemble clustering
dynamic anchor learning
hypergraph reconstruction
Journal
IF:
10.4
Papers:
6.8K
Citations:
3.2W
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