# Similarity and Dissimilarity Guided Co-association Matrix Construction for Ensemble Clustering

arXiv: https://arxiv.org/abs/2411.00904 · HTML: https://mlsanity.com/paper/2411.00904/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Ultra-Scalable Spectral Clustering and Ensemble Clustering](https://mlsanity.com/paper/1903.01057/) (2019)
- [Locally Weighted Ensemble Clustering](https://mlsanity.com/paper/1605.05011/) (2016)
- [Faster Matrix Multiplication via Asymmetric Hashing](https://mlsanity.com/paper/2210.10173/) (2022)
- [Toward Multidiversified Ensemble Clustering of High-Dimensional Data: From Subspaces to Metrics and Beyond](https://mlsanity.com/paper/1710.03113/) (2017)
- [Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities](https://mlsanity.com/paper/1810.12544/) (2018)
- [CDC: A Simple Framework for Complex Data Clustering](https://mlsanity.com/paper/2403.03670/) (2024)
- [Robust Ensemble Clustering Using Probability Trajectories](https://mlsanity.com/paper/1606.01160/) (2016)
- [Weighted Clustering Ensemble: A Review](https://mlsanity.com/paper/1910.02433/) (2019)
- [Ensemble Clustering via Co-association Matrix Self-enhancement](https://mlsanity.com/paper/2205.05937/) (2022)
- [Clustering Ensemble Meets Low-rank Tensor Approximation](https://mlsanity.com/paper/2012.08916/) (2020)

## Built on by (most cited first, 2 of 2)

- [Generalization Performance of Ensemble Clustering: From Theory to Algorithm](https://mlsanity.com/paper/2506.02053/) (2025)
- [CAKE: Confidence in Assignments via K-partition Ensembles](https://mlsanity.com/paper/2602.18435/) (2026)

## Abstract (arXiv)

Ensemble clustering aggregates multiple weak clusterings to achieve a more accurate and robust consensus result. The Co-Association matrix (CA matrix) based method is the mainstream ensemble clustering approach that constructs the similarity relationships between sample pairs according the weak clustering partitions to generate the final clustering result. However, the existing methods neglect that the quality of cluster is related to its size, i.e., a cluster with smaller size tends to higher accuracy. Moreover, they also do not consider the valuable dissimilarity information in the base clusterings which can reflect the varying importance of sample pairs that are completely disconnected. To this end, we propose the Similarity and Dissimilarity Guided Co-association matrix (SDGCA) to achieve ensemble clustering. First, we introduce normalized ensemble entropy to estimate the quality of each cluster, and construct a similarity matrix based on this estimation. Then, we employ the random walk to explore high-order proximity of base clusterings to construct a dissimilarity matrix. Finally, the adversarial relationship between the similarity matrix and the dissimilarity matrix is utilized to construct a promoted CA matrix for ensemble clustering. We compared our method with 13 state-of-the-art methods across 12 datasets, and the results demonstrated the superiority clustering ability and robustness of the proposed approach. The code is available at https://github.com/xuz2019/SDGCA.