基于人工智能的网络时空数据挖掘方法  

A Method for Mining Network Spatiotemporal Data Using Artificial Intelligence

作　　者：姜滟稳[1] JIANG Yan-wen(Wuhu Institute of Technology, Wuhu 241000, China)

机构地区：[1]芜湖职业技术学院,安徽芜湖241000

出　　处：《南京工程学院学报（自然科学版）》2022年第1期43-47,共5页Journal of Nanjing Institute of Technology(Natural Science Edition)

摘　　要：为解决传统数据挖掘应用到网络时空数据挖掘时存在挖掘结果数据召回率低的问题,研究基于人工智能的网络时空数据挖掘方法.采用网络爬虫技术采集网络时空数据,制定网络时空数据关联规则,利用人工智能中的微分进化算法进行挖掘数据筛选及候选集确定,基于用户偏好挖掘数据知识,实现网络时空数据的挖掘.试验结果表明,设计方法在实际应用中能够提高数据召回率,体现挖掘数据的利用价值,实现对数据资源的高效利用.In order to solve the problem of low recall rate of mining results when traditional data mining is applied to network spatiotemporal data mining,this paper examines a network spatiotemporal data mining method based on artificial intelligence.Web crawler technology is used to collect network spatiotemporal data so as to formulate network spatiotemporal data association rules.Network spatiotemporal data is mined by means of filtering mining data and determining candidate sets using differential evolution algorithm in artificial intelligence according to users’preferences.The experimental results show that the method designed in this paper can improve the data recall rate,reflect the utilization value of mined data,and efficiently utilize data resources.

关 键 词：人工智能 微分进化算法 网络 时空 数据 挖掘 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]