Improved graph node embedding and clustering method for fault short text

被引：0
|
作者：
Qiu J. [1 ,2 ]
Sun L. [1 ,2 ]
Han M. [1 ,2 ]
机构：
[1] School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu
[2] Mfg. Indust. Chains Collaboration and Info. Support Technology Key Laboratory of Sichuan Province, Chengdu
来源：
Jisuanji Jicheng Zhizao Xitong/Computer Integrated Manufacturing Systems, CIMS | 2023年 / 29卷 / 12期
关键词：
fault short text; graph node clustering; graph node embedding; local density;
D O I：
10.13196/j.cims.2022.1045
中图分类号：
学科分类号：
摘要：
To effectively mine the cross-text vocabulary association in fault short text, the global feature representation of fault entity nodes was constructed, and the fault entity node clustering label was obtained. An improved graph node embedding and clustering method for fault short text was proposed. In this method, the calculation method of edge weight was innovated in the process of graph construction to distinguish the association between words with different distances under the same window. The graph node structure feature acquisition method was improved to reflect the influence of node value differences on embedding. Then, the structural features and relational features of nodes were fused to enhance the similarity between nodes with similar neighbor nodes. In the clustering stage, a parameter called alternative nodes number was designed to alleviate the sensitivity of cut-off distance. The parameter analysis and performance evaluation were carried out on the open data set and real business data, and the results showed that the proposed method could obtain accurate and effective clustering results of fault entity nodes. © 2023 CIMS. All rights reserved.
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页码：4257 / 4266
页数：9
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