In:
Intelligent Data Analysis, IOS Press, Vol. 27, No. 1 ( 2023-01-30), p. 59-77
Abstract:
A fundamental problem in machine learning is ensemble clustering, that is, combining multiple base clusterings to obtain improved clustering result. However, most of the existing methods are unsuitable for large-scale ensemble clustering tasks owing to efficiency bottlenecks. In this paper, we propose a large-scale spectral ensemble clustering (LSEC) method to balance efficiency and effectiveness. In LSEC, a large-scale spectral clustering-based efficient ensemble generation framework is designed to generate various base clusterings with low computational complexity. Thereafter, all the base clusterings are combined using a bipartite graph partition-based consensus function to obtain improved consensus clustering results. The LSEC method achieves a lower computational complexity than most existing ensemble clustering methods. Experiments conducted on ten large-scale datasets demonstrate 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.
Type of Medium:
Online Resource
ISSN:
1088-467X
,
1571-4128
Language:
Unknown
Publisher:
IOS Press
Publication Date:
2023
detail.hit.zdb_id:
2002356-X
SSG:
24,1
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