基于分块检测的社区网络敏感信息聚类算法  

Clustering algorithm of sensitive information in community network based on block detection

作　　者：王学军 WANG Xuejun(Guangzhou Huali College,Guangzhou 511325,China)

机构地区：[1]广州华立学院,广州511325

出　　处：《智能计算机与应用》2022年第12期214-217,共4页Intelligent Computer and Applications

摘　　要：为了提高无线虚拟社区网络敏感特征信息聚类能力,需要进行数据优化聚类处理,提出基于分块文本相似度检测的无线虚拟社区网络敏感特征信息网格强化聚类算法。采用异构有向图分析方法进行无线虚拟社区网络敏感特征信息存储结构设计,结合特征空间重组技术进行无线虚拟社区网络敏感特征信息结构重组,提取无线虚拟社区网络敏感特征信息的关联信息特征量,采用分块文本相似度检测的方法实现对社区网络敏感特征信息谱密度特征提取和融合聚类处理。仿真结果表明,采用该方法进行社区网络敏感特征信息谱密度融合的聚类性较好,对社区网络敏感信息的分块检测能力较强。In order to improve the clustering ability of sensitive feature information of wireless virtual community network, it is necessary to carry out data optimization clustering processing. This paper proposes a grid strengthening clustering algorithm for sensitive feature information of wireless virtual community network based on block text similarity detection. The heterogeneous directed graph analysis method is used to design the storage structure of sensitive feature information of wireless virtual community network, after that the feature space reorganization technology is used to reorganize the sensitive feature information structure of wireless virtual community network, and the related information feature quantity of sensitive feature information of wireless virtual community network is extracted. The block text similarity detection method is used to realize the spectral density feature extraction and fusion clustering processing of sensitive feature information of community network. The simulation results show that the clustering of sensitive feature information spectral density fusion of community network by this method is good, and the ability of block detection of sensitive information of community network is strong.

关 键 词：社区网络 敏感特征 谱密度 融合聚类 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]