Divide-and-conquer based Large-Scale Spectral Clustering

TL;DR

This paper introduces a divide-and-conquer spectral clustering method that efficiently balances computational cost and clustering quality for large datasets, outperforming existing methods.

Contribution

It proposes a novel landmark selection and approximate similarity matrix approach to reduce complexity in large-scale spectral clustering.

Findings

Lower computational complexity than most existing methods.

Effective clustering results on ten large-scale datasets.

Open-source MATLAB implementation available.

Abstract

Spectral clustering is one of the most popular clustering methods. However, how to balance the efficiency and effectiveness of the large-scale spectral clustering with limited computing resources has not been properly solved for a long time. In this paper, we propose a divide-and-conquer based large-scale spectral clustering method to strike a good balance between efficiency and effectiveness. In the proposed method, a divide-and-conquer based landmark selection algorithm and a novel approximate similarity matrix approach are designed to construct a sparse similarity matrix within low computational complexities. Then clustering results can be computed quickly through a bipartite graph partition process. The proposed method achieves a lower computational complexity than most existing large-scale spectral clustering methods. Experimental results on ten large-scale datasets have demonstrated the efficiency and effectiveness of the proposed method. The MATLAB code of the proposed method and experimental datasets are available at https://github.com/Li-Hongmin/MyPaperWithCode.