The key step of spectral clustering is learning the affinity matrix to measure the similarity among data points. This paper proposes a new spectral clustering method, which uses mutual k nearest neighbor to obtain the affinity matrix by removing the influence of noise. Then, the characteristics of high-dimensional data are self-represented to ensure local important information of data by using affinity matrix in standardized processing. Furthermore, we also use the normalization method to further improve the performance of clustering. Experimental analysis on eight benchmark data sets showed that our proposed method outperformed the state-of-the-art clustering methods in terms of clustering performance such as cluster accuracy and normalized mutual information.
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