摘要
为了提高大规模非结构化数据的分布式存储能力,提出基于空间网格聚类的大规模非结构化数据分布式存储方法.构建大规模非结构化数据多维空间分布式融合模型,采用模糊中心权重聚类的方法进行数据的线性加权控制处理,通过样本扩展和密度融合的方法提取数据特征,采用选择随机性特征分析方法实现对数据特征空间划分,并利用空间网格聚类方法实现大规模非结构化数据分布式存储.仿真结果表明,该方法的执行时间较短,数据聚类准确性较高.该方法有效提高了大规模非结构化数据分布式存储性能,实际应用效果好.
In order to improve the distributed storage capacity of large-scale unstructured data,a distributed storage method of large-scale unstructured data based on spatial grid clustering is proposed.A multi-dimensional spatial distributed fusion model of large-scale unstructured data was constructed.The fuzzy central weight clustering method was used to process the data with linear weighting control.The data features were extracted by the method of sample expansion and density fusion,and the data feature space was divided by the method of selective random feature analysis,the spatial grid clustering method is used to realize the distributed storage of large-scale unstructured data.The simulation results show that the execution time of this method is short and the accuracy of data clustering is high.This method can effectively improve the distributed storage performance of large-scale unstructured data,and the practical application effect is good.
作者
朴承哲
PIAO Chengzhe(Department of National Culture and Vocational Education,Liaoning National Normal College,Shenyang 110032,China)
出处
《太原师范学院学报（自然科学版）》
2021年第3期57-61,共5页
Journal of Taiyuan Normal University:Natural Science Edition
