双层网络中心非显著特征数据挖掘算法  

A Mining Algorithm for Non⁃salient Feature Data in the Center of Bilayer Network

作　　者：庄巧蕙 ZHUANG Qiaohui(College of Information Management,Minnan University of Science and Technology,Shishi Fujian 362700,China)

机构地区：[1]闽南理工学院信息管理学院,福建石狮362700

出　　处：《海南热带海洋学院学报》2024年第5期73-79,共7页Journal of Hainan Tropical Ocean University

基　　金：闽南理工学院校级科研项目(19KJX013)。

摘　　要：为了获取精准的非显著特征数据挖掘结果,提出一种双层网络中心非显著特征数据挖掘算法。首先深入分析双层网络中心的非显著特征信息和类别信息,获取两者之间的相关性和冗余程度。然后根据相关性和冗余程度设定一个判别算子,将判别算子的得分作为标准,实现有效的特征选择。最后在获取双层网络中心非显著特征数据的结构特征后,展开深入的非显著特征数据流拟合处理,同时对非显著特征数据挖掘过程进行跟踪训练,修正挖掘误差,最终完成双层网络中心非显著特征数据挖掘。实验结果表明,采用本文算法可以有效地提升双层网络中心非显著特征数据挖掘结果的准确性。A mining algorithm for non‐salient feature data in the bilayer network’s center was proposed to obtain accu‐rate results of the non‐salient feature data.Firstly,the two‐layer network center’s non‐significant feature and category infor‐mation were analyzed in depth to obtain the correlation and redundancy degree between them.Then a discriminant operator was set according to the degree of correlation and redundancy,and the score of the discriminant operator was used as a criterion to realize effective feature selection.Finally,after obtaining the data structure characteristics of the non‐salient fea‐ture data in the two‐layer network’s center,in‐depth non‐salient feature data stream fitting processing was carried out,and at the same time,the tracking training of the non‐salient feature data mining process was carried out to correct the min‐ing error.Consequently,the non‐salient feature data mining in the two‐layer network’s center was completed.The experimen‐tal results showed that the proposed algorithm can effectively improve the accuracy of the data mining results of non‐sa‐lient features in the double‐layer network’s center.

关 键 词：双层网络中心 非显著特征 数据挖掘 拟合处理 相关性 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]