Analyzing Asymmetric Relationship Between Documents Based on Topic Word Co-occurrence
Zhang Guofang1,2,Wang Xin3,Xu Jianmin1,3()
1School of Management, Hebei University, Baoding 071002, China 2College of Mathematics and Information Science, Hebei University, Baoding 071002, China 3School of Cyber Security and Computer, Hebei University, Baoding 071002, China
[Objective] This paper proposes a quantitative model, aiming to explore the asymmetric relationship between documents. [Methods] Firstly, we examined the asymmetric association between topics with the help of co-occurrence. Secondly, we introduced the concept of the document coverage degree to quantify the asymmetric relationship between documents. Finally, we used document clustering to evaluate the proposed model’s performance. [Results] Compared with two existing measurement models, the average value of clustering was reduced by up to 22.6% and 23.3% with the proposed model. [Limitations] The proposed model only analyzed textual contents, which did not include pictures and formulas. [Conclusions] The proposed model could effectively improve the accuracy of document clustering.
张国防, 王鑫, 徐建民. 基于主题词共现的文档非对称关系量化研究*[J]. 数据分析与知识发现, 2023, 7(3): 110-120.
Zhang Guofang, Wang Xin, Xu Jianmin. Analyzing Asymmetric Relationship Between Documents Based on Topic Word Co-occurrence. Data Analysis and Knowledge Discovery, 2023, 7(3): 110-120.
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