Spectral Clustering based on JS']JS-divergence for Uncertain Data

被引：0

作者：

Wang, Yingxu ^{[1
]}

Dong, Jiwen ^{[1
]}

Zhou, Jin ^{[1
]}

Wang, Lin ^{[1
]}

Han, Shiyuan ^{[1
]}

Zhang, Tong ^{[2
]}

Chen, C. L. Philip ^{[3
]}

机构：

[1] Univ Jinan, Shandong Prov Key Lab Network Based Intelligent C, Jinan 250022, Shandong, Peoples R China

[2] South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510006, Guangdong, Peoples R China

[3] Univ Macau, Fac Sci & Technol, Macau, Peoples R China

来源：

2017 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC) | 2017年

基金：

中国国家自然科学基金;

关键词：

uncertain data clustering; !text type='JS']JS[!/text]-divergence; spectral clustering method; ALGORITHMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1972 / 1975

页数：4

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