基于深度数据挖掘的高校信息数据整合方法  

University information data integration method based on deep data mining

作　　者：李伊 LI Yi(Hunan Post and Telecommunication College,Changsha 410015,China)

机构地区：[1]湖南邮电职业技术学院,长沙410015

出　　处：《信息技术》2023年第12期48-51,56,共5页Information Technology

基　　金：湖南省十三五规划课题(XJK19BZY029)。

摘　　要：针对高效信息数据整合方法中未能使用Z-score算法对信息数据进行标准化处理,导致信息数据整合时存在整合精度低、时间长等问题,提出基于深度数据挖掘的高校信息数据整合方法。首先依据Z-score算法标准化信息数据,并依据标准化结果提取信息数据特征;再使用深度数据挖掘方法中的K-means算法计算数据特征生成基聚类器;最后将信息数据放入基聚类器中进行聚类;实现信息数据的整合。实验结果表明,运用该方法进行数据整合时,整合精度高、整合时长较短。In the efficient information data integration method,due to the failure to use Z-score algorithm to standardize information data,there are some problems in information data integration,such as low integration accuracy and long time.Therefore,a method of university information data integration based on deep data mining is proposed.Firstly,the information data is standardized according to Z-score algorithm,and the characteristics of information data are extracted according to the standardization results;Then the k-means algorithm in the deep data mining method is used to calculate the data features and generate the base cluster;Finally,the information data is put into the base cluster for clustering;Realize the integration of information and data.The experimental results show that this method has high integration accuracy and short integration time.

关 键 词：深度数据挖掘方法 高校信息数据 整合方法 基聚类器 Z-score算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]