Divide-and-conquer based Large-Scale Spectral Clustering

2 May 2021  ·  Hongmin Li, Xiucai Ye, Akira Imakura, Tetsuya Sakurai ·

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. Experimental results on ten large-scale datasets have demonstrated the efficiency and effectiveness of the proposed methods. The MATLAB code of the proposed method and experimental datasets are available at https://github.com/Li- Hongmin/MyPaperWithCode.

PDF Abstract

Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Image/Document Clustering pendigits DnC-SC Accuracy (%) 82.27 # 1
runtime (s) 0.64 # 1
NMI 82.86 # 1
Image Clustering pendigits DnC-SC Accuracy 0.8201 # 2
NMI 0.8201 # 2
Image Clustering USPS DnC-SC NMI 0.8286 # 14
Accuracy 0.8255 # 12