# LSEC: Large-scale spectral ensemble clustering

arXiv: https://arxiv.org/abs/2106.09852 · HTML: https://mlsanity.com/paper/2106.09852/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 46 references that are in the map.
- First posted on arXiv 18 Jun 2021.

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

- [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)
- [Enhanced Ensemble Clustering via Fast Propagation of Cluster-wise Similarities](https://mlsanity.com/paper/1810.12544/) (2018)
- [Robust Ensemble Clustering Using Probability Trajectories](https://mlsanity.com/paper/1606.01160/) (2016)
- [Divide-and-conquer based Large-Scale Spectral Clustering](https://mlsanity.com/paper/2104.15042/) (2021)
- [Fast consensus clustering in complex networks](https://mlsanity.com/paper/1902.04014/) (2019)
- [Combining Multiple Clusterings via Crowd Agreement Estimation and Multi-Granularity Link Analysis](https://mlsanity.com/paper/1405.1297/) (2014)

## Built on by (most cited first, 1 of 1)

- [Eigenvalue-based Incremental Spectral Clustering](https://mlsanity.com/paper/2308.10999/) (2023)

## Abstract (arXiv)

Ensemble clustering is a fundamental problem in the machine learning field, combining multiple base clusterings into a better clustering result. However, most of the existing methods are unsuitable for large-scale ensemble clustering tasks due to the efficiency bottleneck. In this paper, we propose a large-scale spectral ensemble clustering (LSEC) method to strike a good balance between efficiency and effectiveness. In LSEC, a large-scale spectral clustering based efficient ensemble generation framework is designed to generate various base clusterings within a low computational complexity. Then all based clustering are combined through a bipartite graph partition based consensus function into a better consensus clustering result. The LSEC method achieves a lower computational complexity than most existing ensemble clustering methods. Experiments conducted on ten large-scale datasets show the efficiency and effectiveness of the LSEC method. The MATLAB code of the proposed method and experimental datasets are available at https://github.com/Li- Hongmin/MyPaperWithCode.