摘要
为了提高异质网络下正态云模型的数据挖掘能力,提出基于相似度度量的异质网络下正态云模型数据挖掘模型,提取异质网络下正态云模型的模糊粗糙集特征量,采用相似度特征分析方法进行异质网络下正态云模型的大数据融合处理,建立异质网络正态云分布式结构重组模型,通过特征空间重构方法分析异质网络正态云关联规则特征集,构建异质网络正态云模型数据的输出自相关特征匹配模型,结合模糊特征聚类分析方法进行异质网络正态云模型数据的统计分析,通过模糊相关性匹配方法实现异质网络下正态云模型相似度度量.仿真结果表明,采用该方法进行异质网络下正态云模型相似度度量的准确度较高,模糊辨识能力较高,提高了异质网络下正态云模型的准确挖掘性能.
To improve the data mining ability of normal cloud model in heterogeneous network,the data mining model of normal cloud model in heterogeneous network based on similarity measure is used to extract the fuzzy rough set feature quantity of normal cloud model in heterogeneous network.Big data fusion of normal cloud model in heterogeneous network is carried out by using similarity feature analysis method,and the distributed structure reorganization model of normal cloud in heterogeneous network is established.The feature set of normal cloud association rules in heterogeneous networks is analyzed by feature space reconstruction method,and the output correlation feature matching model of heterogeneous network normal cloud model data is constructed.combined with fuzzy feature clustering analysis method,the statistical analysis of heterogeneous network normal cloud model data is carried out,and the similarity degree of normal cloud model in heterogeneous network is measured by fuzzy correlation matching method.The simulation results show that the accuracy of using this method to measure the similarity of normal cloud model in heterogeneous network is high,and the fuzzy identification ability is high,which improves the accurate mining performance of normal cloud model in heterogeneous network.
作者
汪明珠
毛德梅
WANG Ming-zhu;MAO De-mei(Experiment and Training Teaching Management Department,West Anhui University,Liu’an 237012,China;School of Electronic and Information Engineering,West Anhui University,Liu’an 237012,China)
出处
《内蒙古民族大学学报（自然科学版）》
2020年第4期310-315,共6页
Journal of Inner Mongolia Minzu University：Natural Sciences
基金
安徽省高校省级质量工程项目(2018xfsyxm041)。
关键词
异质网络
正态云模型
相似度
度量
粗糙集
Heterogeneous network
Normal cloud model
Similarity
Measurement
Rough set
