自适应谱聚类研究的论文，可供科研人员参考-Spectral clustering hasbeenusedto identify clustersthat arenon
linearly separableininput space, andusuallyoutper


formstraditional clustering algorithms. However, the performances of spectral clustering are severely dependent onvaluesof the

scaling parameter. Inthispaper, anadaptive spectral clustering(ASC) algorithmwasproposedbasedontraditional spectral clus


tering, whichcanchoose the scaling parameter automatically by using techniques similar to kernel selection. The newalgorithm

was comparedto existingparameter selectionbasedspectral clustering algorithmsonbothsyntheticandUCI datasets, andthe ex


perimental results validatethe effectivenessof theproposedalgorithm