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| An Improved Spectral Clustering Algorithm Based on Axiomatic Fuzzy Set |
| ZHAO Xiaoqiang①②③ LIU Xiaoli① |
①(College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, China)
②(Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, China)
③(National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, China) |
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Abstract Gaussian kernel is usually used as the similarity measure in spectral clustering algorithm, and all the available features are used to construct the similarity matrix with Euclidean distance. The complexity of the data set would affect its spectral clustering performance. Therefore, an improved spectral clustering algorithm
based on Axiomatic Fuzzy Set (AFS) is proposed. Firstly, AFS algorithm is combined to measure the similarity of more suitable data by recognizing features, and the stronger affinity matrix is generated. Then Nyström sampling algorithm is used to calculate the similarity matrix between the sampling points and the sampling points and the remaining points to reduce the computational complexity. Finally, the experiment is carried out by using different data sets and image segmentations, the effectiveness of the proposed algorithm are proved.
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Received: 25 September 2017
Published: 30 May 2018
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| Fund:The National Natural Science Foundation of China (61763029), The Gansu Province Basic Research Innovation Group Fund (1506RJIA031) |
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Corresponding Authors:
ZHAO Xiaoqiang
E-mail: xqzhao@lut.cn
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