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<p>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.</p>

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A local adaptive fuzzy spectral clustering method for robust and practical clustering

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月6日

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  • Qiangguo Yu,
  • Liangquan Jia,
  • Yuxuan Shao,
  • Jianhao He,
  • Jinsheng Wang,
  • Xinhui Yuan,
  • Miao Huan,
  • Yi Yang

摘要

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.