[1]CHU Derun,ZHOU Zhiping.Shared nearest neighbor adaptive spectral clustering algorithm based on axiomatic fuzzy set theory[J].CAAI Transactions on Intelligent Systems,2019,14(5):897-904.[doi:10.11992/tis.201810002]
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Shared nearest neighbor adaptive spectral clustering algorithm based on axiomatic fuzzy set theory

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 897-904 Column: 学术论文—机器学习 Public date: 2019-09-05
Title:
Shared nearest neighbor adaptive spectral clustering algorithm based on axiomatic fuzzy set theory
Author(s):
CHU Derun;  ZHOU Zhiping
Engineering Research Center of Internet of Things Technology Applications Ministry of Education, Jiangnan University, Wuxi 214122, China
Keywords:
machine learning; data mining; clustering analysis; fuzzy clustering; spectral clustering; axiomatic fuzzy set theory; shared nearest neighbor; scale parameter
CLC:
TP18
DOI:
10.11992/tis.201810002
Abstract:
For the traditional spectral clustering algorithm, the Gaussian kernel function is usually used as the similarity measure. However, the similarity of distance cannot fully express the ambiguity, uncertainty, and complexity inherent in the original data, resulting in the reduction of clustering performance. To solve this problem, we propose an axiomatic fuzzy set shared nearest neighbor adaptive spectral clustering algorithm. First, the proposed algorithm uses a fuzzy similarity measurement method based on axiomatic fuzzy set theory to measure more suitable data pairwise similarity by identifying features. Then, the structure and density information of sample point distribution in a dense area is obtained using the method of sharing the nearest neighbor, and the parameter σ is automatically adjusted according to the density degree of each point in the domain, thereby generating a more powerful affinity matrix to further increase the accuracy rate of clustering. Experimental results show that the proposed algorithm has better clustering performance than distance spectral clustering, adaptive spectral clustering, fuzzy clustering, and landmark spectral clustering.
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