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.