UM
Spectral Clustering based on JS-divergence for Uncertain Data
Wang, Yingxu; Dong, Jiwen; Zhou, Jin; Wang, Lin; Han, Shiyuan; Zhang, Tong; Chen, C. L. Philip; IEEE
2017
Conference Name2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Pages1972-1975
Publication Place345 E 47TH ST, NEW YORK, NY 10017 USA
PublisherIEEE
AbstractSpectral clustering is one of the most effective methods of data mining, in which the adjacency matrix is constructed by using the similarity matrix. In this paper, to extend spectral clustering method for uncertain data clustering, we propose a new spectral clustering method based on JS-divergence. In the proposed method, the JS-divergence is used to construct the adjacency matrix in the spectral clustering, which is more suitable to calculate the similarity between uncertain data objects as a symmetrical measurement compared to the KL-divergence.
Keyworduncertain data clustering JS-divergence spectral clustering method
URLView the original
Indexed ByCPCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000427598702002
The Source to ArticleWOS
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Citation statistics
Document TypeConference paper
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Wang, Yingxu,Dong, Jiwen,Zhou, Jin,et al. Spectral Clustering based on JS-divergence for Uncertain Data[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2017:1972-1975.
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