量化关联规则在高校就业信息数据中的应用  

Application of Quantitative Association Rules in College Employment Information Data

作　　者：张晓萍[1] 朱玉全[1] 陈耿[2] 

机构地区：[1]江苏大学计算机科学与通信工程学院,江苏镇江212013 [2]南京审计学院信息科学学院,江苏南京210029

出　　处：《计算机技术与发展》2013年第11期199-202,212,共5页Computer Technology and Development

基　　金：国家自然科学基金资助项目(71271117);江苏省科技型企业技术创新资金项目(BC2012201)

摘　　要：针对就业信息数据中存在着大量的量化属性和分类属性等现象,提出了一种基于k-means的量化关联规则挖掘方法。该方法利用聚类算法k-means对量化属性进行合理分区,将量化属性转化为布尔型;利用改进的布尔关联规则方法对此进行关联规则挖掘,找出学生的受教育属性和就业属性之间的关联性;对挖掘出的规则进行分析和运用。就业信息数据实验证明,文中所提方法对就业信息进行挖掘是有效的、可行的。In view of the phenomenon such as a lot of quantitative attributes and categorical attributes among the employment information data,proposed an algorithm for mining quantitative association rules based on k-means. This method uses k-means clustering algorithm to partition the quantitative attributes reasonably and convert quantitative attributes to Boolean type;use the improved Boolean association rules method to conduct mining association rules on this to find the correlation between student＇ s educational attributes and employment attributes;analyze and apply the rules. Employment information data experimental results show that the presented method is effective and feasible in mining the employment information data.

关 键 词：数据挖掘 量化关联规则 k—means聚类算法 就业信息 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]