Fuzzy c-medoids Method based on JS']JS-divergence for Uncertain Data Clustering

被引：0

作者：

Wang, Yingxu ^{[1
]}

Dong, Jiwen ^{[1
]}

Zhou, Jin ^{[1
]}

Wang, Dong ^{[1
]}

Wang, Lin ^{[1
]}

Han, Shiyuan ^{[1
]}

Chen, Yuehui ^{[1
]}

机构：

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

来源：

2017 4TH INTERNATIONAL CONFERENCE ON INFORMATION, CYBERNETICS AND COMPUTATIONAL SOCIAL SYSTEMS (ICCSS) | 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; Fuzzy c-medoids method; ALGORITHM;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Uncertain data clustering is one significant research in data mining. Many similarity measurements of uncertain objects are proposed. Traditional clustering methods can be extended with these new similarity measurements. In this paper, we propose a new fuzzy c-medoids method for uncertain data clustering, named UFC-medoids. The JS-divergence is used as the similarity measurement between uncertain objects in this algorithm. In the experiments on synthetic datasets, the presented algorithm has shown a good performance.

引用

页码：312 / 315

页数：4

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