摘要
传统用户信息数据挖掘模型单一因素在分析过程中的影响占比较大,得到的分析结果准确性有所偏差,因此,设计一种基于物联网技术的一卡通用户信息数据挖掘模型。通过RFID射频识别设备的电子标签和标签阅读器完成一卡通用户的行为数据采集,经过数据清洗、集成、变换和消减将数据整理成为目标数据集,在此基础上,构建行为数据仓库,设计数据库表结构,最后将关联规则与关联矩阵相结合,对数据挖掘算法进行优化,将各个因素之间存在的规则进行综合评价,提高数据挖掘结果的准确性。为验证设计模型的有效性进行了实验,实验结果表明,设计的数据挖掘模型在对贫困生进行认定时,得到的结果与实际情况基本相符,验证了设计的数据挖掘模型分析结果准确性较高。
Traditional user information data mining model single factor in the analysis process is relatively large,and the accuracy of the analysis results is biased.Therefore,a one card user information data mining model based on Internet of things technology is designed.The behavior data of all-in-one card users is collected by RFID tags and tag readers.After data cleaning,integration,transformation and reduction,the data is sorted into the target data set.On this basis,the behavior data warehouse is constructed,the database table structure is designed,and the association rules and association matrix are combined to optimize the data mining algorithm The existing rules among various factors are comprehensively evaluated to improve the accuracy of data mining results.In order to verify the effectiveness of the design model,the experimental results show that the designed data mining model in the identification of poor students,the results are basically consistent with the actual situation,which verifies that the designed data mining model analysis results are more accurate.
作者
周挺
ZHOU Ting(Xi’an Aeronautical Polytechnic Institute,Xi’an 710089,China)
出处
《自动化与仪器仪表》
2021年第3期58-60,64,共4页
Automation & Instrumentation
基金
西安航空职业技术学院自然科学类科研项目:“语音编程教学软件研究与设计”(No.19XHZK-022)。
关键词
物联网技术
一卡通用户信息
数据挖掘模型
关联规则
关联矩阵
internet of things technology
all in one card user information
data mining model
association rules
association matrix
