网络信息大数据动态增量分布式挖掘方法研究  

Research on Dynamic Incremental Distributed Mining Method of Network Information Big Data

作　　者：梅莹莹 MEI Yingying(Anhui Sanlian University,Hefei 230601,China)

机构地区：[1]安徽三联学院,合肥230601

出　　处：《安阳工学院学报》2020年第2期41-44,66,共5页Journal of Anyang Institute of Technology

基　　金：安徽三联学院2018年校级自然科学平台重点项目“基于机器学习的个性化信息检索技术研究”(PTZD2018004);安徽省教学研究重点项目“OBE理念下应用型本科计算机硬件课程群建设研究与实践”(2018jyxm0445);安徽省大规模在线开放课程(MOOC)示范项目“Java语言程序设计”(2018mooc127)。

摘　　要：为了提高对网络信息大数据动态增量分布式检测和挖掘能力,提出基于相似度特征提取的网络信息大数据动态增量分布式挖掘方法。构建网络信息大数据的异构存储结构模型,采用模糊分布式检测方法进行网络信息大数据动态增量特征分布式检测,提取网络信息大数据的相似度特征量,采用模糊聚类方法进行网络信息大数据动态增量分布式融合聚类处理,在模糊聚类中心进行网络信息大数据动态增量分布式检测和识别,实现网络信息大数据动态增量分布式挖掘。仿真结果表明,采用该方法进行网络信息大数据动态增量分布式挖掘的准确性较高,数据挖掘的查准率较高,识别能力较好。In order to improve the ability of dynamic incremental distributed detection and mining of network information big data, a dynamic incremental distributed mining method of network information big data based on similarity feature extraction is proposed. The heterogeneous storage structure model of network information big data is constructed, the dynamic incremental feature of network information big data is detected by fuzzy distributed detection method, the similarity feature quantity of network information big data is extracted, the dynamic incremental distributed fusion clustering of network information is carried out by using fuzzy clustering method,and the dynamic incremental distributed detection and identification of network information is carried out in fuzzy clustering center. Realize dynamic incremental distributed mining of network information big data. The simulation results show that the accuracy of big data dynamic incremental distributed mining is high, the precision of data mining is high, and the recognition ability is good.

关 键 词：网络信息 大数据 分布式挖掘 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]