虚拟社区网络低维冗余数据快速挖掘系统设计  

Design of low dimensional redundant data mining system in virtual community network

作　　者：鲁江[1] LU Jiang(Xi’an Medical University,Xi’an 710021,China)

机构地区：[1]西安医学院,陕西西安710021

出　　处：《电子设计工程》2024年第4期80-83,88,共5页Electronic Design Engineering

摘　　要：虚拟社区网络低维冗余数据本体结构属性难以确定,导致不能挖掘出全部的低维冗余数据,为此设计了虚拟社区网络低维冗余数据快速挖掘系统。利用基于ARM72TDI芯片的数据采集器获取原始数据,选取适当的方式进行数据读取与传送。采用自适应特征调度方法构建低维冗余数据聚类相似性函数,通过用四阶累积量切片压缩处理数据。引入权重因素,计算虚拟社区网络节点间相似度,分析虚拟社区网络用户之间多重关系。采用邻域空间交叉融合方法提取数据本体结构属性,以此实现低维冗余数据挖掘。测试结果表明,该系统能够挖掘出全部低维冗余数据,数据挖掘总耗时为21.3 s,说明该系统能够全面、快速挖掘出低维冗余数据。Because it is difficult to determine the structural attributes of low dimensional redundant data ontology in virtual community network,it is impossible to mine all the low dimensional redundant data.Use the data collector based on ARM72TDI chip to obtain the original data,and select the appropriate way to read and transmit the data.The clustering similarity function of low dimensional redundant data is constructed by using the adaptive feature scheduling method,and the data is compressed by using the fourth order cumulant slice.The weight factor is introduced to calculate the similarity between virtual community network nodes and analyze the multiple relationships among virtual community network users.The structural attributes of data ontology are extracted by the method of neighborhood space cross fusion,so as to realize low dimensional redundant data mining.The test results show that the system can mine all low dimensional redundant data,and the total time for data mining is 21.3 s,which shows that the system can comprehensively and quickly mine low dimensional redundant data.

关 键 词：虚拟社区网络 低维冗余数据 快速挖掘 四阶累积量 本体结构属性 

分 类 号：TN912[电子电信—通信与信息系统]