摘要
由于异构网络环境下,数据特征多样化,导致对其进行挖掘的精度较低,提出基于定量递归分组特征提取的异构环境下多元化海量数据精准挖掘方法.在异构环境下,构建大数据统计特征提取模型,提取多元化海量数据,结合数据库关联度特征,通过相空间重构方法对多元化海量数据的特征重构;通过量化分析,对重构后的多元化海量数据样本分组,并对样本分组检测;通过递归图分析方法提取多元化海量数据挖掘的分组特征,通过模糊控制方法,根据特征分布的聚类性实现对异构环境下多元化海量数据的准确挖掘.仿真结果表明,采用该方法进行异构环境下多元化海量数据挖掘的精度较高,特征融合聚类性较好,有效提高多元化海量数据的查准性和查全性.
Due to the diversity of data features in a heterogeneous network environment,the accuracy of mining it is relatively low.A method for accurate mining of multiple massive data in a heterogeneous environment based on quantitative recursive grouping feature extraction is proposed.In a heterogeneous environment,construct a statistical feature extraction model for big data,extract diversified mass data,combine the characteristics of database correlation,and reconstruct the features of the diversified mass data through the phase space reconstruction method.Diversified and massive data samples are grouped,and the samples are grouped and detected;the recursive graph analysis method is used to extract the grouping features of the diversified massive data mining,and the fuzzy control method is used to achieve the diversity of heterogeneous environments based on the clustering of feature distributions.Accurate mining of massive data.The simulation results show that the proposed method has higher accuracy for multivariate mass data mining in heterogeneous environments,better feature fusion and clustering,and effectively improves the accuracy and completeness of multivariate mass data.
作者
周红志
ZHOU Hongzhi(College of Information Engineering,Fuyang Normal University,Fuyang 236041,China)
出处
《周口师范学院学报》
CAS
2020年第5期62-65,共4页
Journal of Zhoukou Normal University
关键词
异构环境
多元化海量数据
精准挖掘
特征提取
heterogeneous environment
diversity of mass data
precision mining
feature extraction