An Improved Spectral Clustering Algorithm Based on Axiomatic Fuzzy Set

Xiaoqiang ZHAO; Xiaoli LIU

doi:10.11999/IEIT170904

Volume 40 Issue 8

Aug. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(8): 1904-1910

Xiaoqiang ZHAO, Xiaoli LIU. An Improved Spectral Clustering Algorithm Based on Axiomatic Fuzzy Set[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1904-1910. doi: 10.11999/IEIT170904

Citation:

Xiaoqiang ZHAO, Xiaoli LIU. An Improved Spectral Clustering Algorithm Based on Axiomatic Fuzzy Set[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1904-1910. doi: 10.11999/IEIT170904

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An Improved Spectral Clustering Algorithm Based on Axiomatic Fuzzy Set

doi: 10.11999/IEIT170904

Xiaoqiang ZHAO^{1, 2, 3
,
,},
Xiaoli LIU¹

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

Funds: The National Natural Science Foundation of China (61763029), The Gansu Province Basic Research Innovation Group Fund (1506RJIA031)

Received Date: 2017-09-25
Rev Recd Date: 2018-05-02

Available Online: 2018-05-30

Publish Date: 2018-08-01

Abstract

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.
- Affinity matrix,
- Spectral clustering,
- Axiomatic Fuzzy Set (AFS),
- Nyström sampling algorithm

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References(22)

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