Abstract:

Spectral clustering has been used to identify clusters that are non-linearly separable in input space, and usually outperforms traditional clustering algorithms. However, the performances of spectral clustering are severely dependent on values of the scaling parameter. In this paper, an adaptive spectral clustering (ASC) algorithm was proposed based on traditional spectral clustering, which can choose the scaling parameter automatically by using techniques similar to kernel selection. The new algorithm was compared to existing parameter selection based spectral clustering algorithms on both synthetic and UCI data sets, and the experimental results validate the effectiveness of the proposed algorithm.

Key words: adaptive; spectral clustering; parameter selecuion