LSEC: Large-scale spectral ensemble clustering

TL;DR

LSEC introduces a scalable spectral ensemble clustering approach that efficiently combines multiple clusterings into a superior result, suitable for large datasets, balancing computational speed and clustering quality.

Contribution

The paper presents a novel large-scale spectral ensemble clustering method that reduces computational complexity while maintaining high clustering effectiveness.

Findings

LSEC outperforms existing methods in efficiency on large datasets.

The method achieves comparable or better clustering quality.

Experiments validate the scalability and effectiveness of LSEC.

Abstract

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.