基于模糊聚类算法的多源异构数据中台智能整合方法  

Intelligent Integration Method of Multi-source Heterogeneous Data Based on Fuzzy Clustering Algorithm

作　　者：姚思明 YAO Siming(Xiamen Big Data Co,Ltd,Fwian Xiamen 361000 China)

机构地区：[1]厦门大数据有限公司,福建厦门361000

出　　处：《长江信息通信》2024年第9期59-61,共3页Changjiang Information & Communications

摘　　要：传统的数据整合方法往往无法有效地处理这些异构数据,因此需要一种更为智能的方法来解决这个问题,现提出基于模糊聚类算法的多源异构数据中台智能整合方法。首先,基于模糊聚类算法提取多源异构数据特征,对这些数据进行归类,然后采取适当的处理策略,其次,生成数据中台智能整合函数,汇总并处理数据中台智能整合函数的节点,最后,实现多源异构数据中台的智能聚合。实验结果表明:该实验以20min为单位时长,该数据整合的速率在整体实验中,相较于传统方法,该文方法的数据整合速率明显优于传统方法,证明基于模糊聚类算法的多源异构数据中台智能整合方法在处理大规模多源异构数据时仍具有较高的效率。Traditional data integration methods often fail to deal with these heterogeneous data effectively,so a more intelligent method is nceded to solve the problem.Now,the intelligent integration method of multi-source heterogencous data middle platform based on fuzzy clustering algorithm is proposcd.First,the features of multi-source heterogencous data are extracted based on fuzzy clustering algorithm,the data are classified,and then appropriate processing strategies are adopted.Second,the intelligent integration function of data center is generated,and the nodes of intelligent integration function of data center are summarized and processed.Finally,the intelligent aggregation of multi-source heterogencous data center is realized.Experimental results show that the experiment in 20min,the rate of data integration in the overall experiment,compared with the traditional method,the method of data integration rate is significantly better than the traditional method,prove that based on the fuzzy clustering algorithm of multi-source heterogencous data in intelligent integration method in dealing with large-scale multi-source heterogeneous data still has high efficiency.

关 键 词：数据中台智能整合 多源异构 数据中台 模糊聚类算法 

分 类 号：D26.4[政治法律—政治学]