基于差分进化算法的网络多属性大数据聚类挖掘方法

Network multi-attribute big data clustering mining method based on differential evolution algorithm

为了提高网络多属性大数据聚类挖掘的精度,提出基于差分进化算法的网络多属性大数据聚类挖掘方法.首先,构建网络多属性大数据参数采集模型,并对其进行动态结构性重组,从而实现大数据存储结构分析.其次,提取大数据聚类特征参数,结合快速特征收敛性控制方法,建立网络多属性大数据特征分析模型.通过灰度特征信息重组方法,对大数据聚类特征进行空间网格分块融合处理.最后,采用差分进化算法进行大数据特征参数的聚类挖掘寻优,从而实现对网络多属性大数据的特征聚类和可靠性挖掘.仿真结果表明,采用该方法进行网络多属性大数据挖掘,分类正确率和挖掘识别率均较高,能够有效进行网络多属性大数据的聚类挖掘.

In order to improve the accuracy of network multi-attribute big data clustering mining,a network multi-attribute big data clustering mining method based on differential evolution algorithm is proposed.The parameter collection model of network multi-attribute big data is constructed and the dynamic structure is reorganized to realize big data storage structure analysis.The network multi-attribute big data feature analysis model is established which combined storage structure is used to extract the process of big data clustering feature parameters and the method of fast feature convergence control.The big data clustering feature is used for the big data spatial grid block fusion processing through the gray-scale feature information reorganization method.The feature clustering and reliability mining of network multi-attribute big data is realized according to the differential evolution method to perform clustering and optimization of big data feature parameters.The simulation results show that the classification accuracy and mining recognition rate of this method for network multi-attribute big data mining is high,and it can effectively perform network multi-attribute big data clustering mining.