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• W2772093198 abstract "Spectral 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." @default.
• W2772093198 created "2017-12-22" @default.
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• W2772093198 date "2017-10-01" @default.
• W2772093198 modified "2023-09-26" @default.
• W2772093198 title "Spectral clustering based on JS-divergence for uncertain data" @default.
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• W2772093198 doi "https://doi.org/10.1109/smc.2017.8122907" @default.
• W2772093198 hasPublicationYear "2017" @default.
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