Finding Community Structure of Bayesian Networks by Improved K-means Algorithm

被引：0
|
作者：
Wang Manxi [1 ]
Wang Liandong [1 ]
Wang Chenfeng [2 ]
Gao Xiaoguang [2 ]
Di Ruohai [2 ]
机构：
[1] State Key Lab Complex Electromagnet Environm Effe, Luoyang, Henan, Peoples R China
[2] Northwestern Polytech Univ, Xian, Shanxi, Peoples R China
来源：
2018 IEEE 3RD INTERNATIONAL CONFERENCE ON IMAGE, VISION AND COMPUTING (ICIVC) | 2018年
关键词：
Bayesian network; community structure; improved K-means algorithm;
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：
Many Bayesian networks display community structure; study community structure helps to reduce the complexity of learning Bayesian network in high-dimensional data. To find the community structure of Bayesian networks, this paper proposes an improved K-means algorithm by incorporating the mutual information into the K-means algorithm and modifying the iteration conditions. Experiments show that the proposed algorithm is suitable for block tasks of Bayesian network. Compared with FastNewman algorithm, the proposed algorithm can obtain accurate results in a shorter time.
引用
收藏
页码：865 / 869
页数：5
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