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Current spectral clustering algorithms suffer from both sensitivity to scaling parameter selection in similarity matrix construction, and data perturbation. This paper aims to improve robustness in clustering algorithms and combat these two limitations based on heat kernel theory. Heat kernel can statistically depict traces of random walk, so it has an intrinsic connection with diffusion distance, with which we can ensure robustness during any clustering process. By integrating heat distributeddoi:10.1109/icdm.2011.15 dblp:conf/icdm/HuangYQY11 fatcat:db2jhldn7nfspbnjkrsgewy5za