A local adaptive fuzzy spectral clustering method for robust and practical clustering
Traditional spectral clustering algorithms are sensitive to the similarity matrix, which impacts their performance. To address this, a local adaptive fuzzy spectral clustering (FSC) method is introduced, incorporating a fuzzy index to reduce this sensitivity. FSC also simplifies the traditional process through a local adaptive framework, optimizing the similarity matrix's use. Experimental results show that FSC outperforms traditional methods, particularly on high-dimensional datasets with complex structures.
Authors
- Yi‐Ching Yang (ORCID: https://orcid.org/0000-0003-1391-8040)
- Liangquan Jia (ORCID: https://orcid.org/0000-0001-9627-1988)
- Jiyuan He (ORCID: https://orcid.org/0009-0008-8126-8290)
- Qiangguo Yu (ORCID: https://orcid.org/0000-0002-1331-3140)
- Xinhui Yuan
- Jinsheng Wang (ORCID: https://orcid.org/0000-0001-9954-2720)
- Yuxuan Shao
- Miao Huan
Institutions
- Huzhou Normal University (CN)
- Zhejiang University of Technology (CN)
Publication Details
- Journal
- Scientific Reports
- Published
- 2025-03-06
- DOI
- https://doi.org/10.1038/s41598-025-91812-4
- Citations
- 3
- Primary Topic
- Advanced Clustering Algorithms Research
- Type
- article
- Field-Weighted Citation Impact
- 7.44
Funders
- Natural Science Foundation of Zhejiang Province