  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.
