# Ensemble Clustering via Co-association Matrix Self-enhancement

arXiv: https://arxiv.org/abs/2205.05937 · HTML: https://mlsanity.com/paper/2205.05937/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 7 of 7)

- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [A Refined Laser Method and Faster Matrix Multiplication](https://mlsanity.com/paper/2010.05846/) (2020)
- [Locally Weighted Ensemble Clustering](https://mlsanity.com/paper/1605.05011/) (2016)
- [Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities](https://mlsanity.com/paper/1810.12544/) (2018)
- [Robust Principal Component Analysis on Graphs](https://mlsanity.com/paper/1504.06151/) (2015)
- [Robust Ensemble Clustering Using Probability Trajectories](https://mlsanity.com/paper/1606.01160/) (2016)

## Built on by (most cited first, 6 of 6)

- [DivClust: Controlling Diversity in Deep Clustering](https://mlsanity.com/paper/2304.01042/) (2023)
- [Inaccurate Label Distribution Learning](https://mlsanity.com/paper/2302.13000/) (2023)
- [k-HyperEdge Medoids for Clustering Ensemble](https://mlsanity.com/paper/2412.08289/) (2024)
- [Similarity and Dissimilarity Guided Co-association Matrix Construction for Ensemble Clustering](https://mlsanity.com/paper/2411.00904/) (2024)
- [Generalization Performance of Ensemble Clustering: From Theory to Algorithm](https://mlsanity.com/paper/2506.02053/) (2025)
- [Towards Better IncomLDL: We Are Unaware of Hidden Labels in Advance](https://mlsanity.com/paper/2511.12494/) (2025)

## Abstract (arXiv)

Ensemble clustering integrates a set of base clustering results to generate a stronger one. Existing methods usually rely on a co-association (CA) matrix that measures how many times two samples are grouped into the same cluster according to the base clusterings to achieve ensemble clustering. However, when the constructed CA matrix is of low quality, the performance will degrade. In this paper, we propose a simple yet effective CA matrix self-enhancement framework that can improve the CA matrix to achieve better clustering performance. Specifically, we first extract the high-confidence (HC) information from the base clusterings to form a sparse HC matrix. By propagating the highly-reliable information of the HC matrix to the CA matrix and complementing the HC matrix according to the CA matrix simultaneously, the proposed method generates an enhanced CA matrix for better clustering. Technically, the proposed model is formulated as a symmetric constrained convex optimization problem, which is efficiently solved by an alternating iterative algorithm with convergence and global optimum theoretically guaranteed. Extensive experimental comparisons with twelve state-of-the-art methods on eight benchmark datasets substantiate the effectiveness, flexibility and efficiency of the proposed model in ensemble clustering. The codes and datasets can be downloaded at https://github.com/Siritao/EC-CMS.