[1]张晓萍,朱玉全,陈耿.量化关联规则在高校就业信息数据中的应用[J].计算机技术与发展,2013,(11):199-202.
　ZHANG Xiao-ping[],ZHU Yu-quan[],CHEN Geng[].Application of Quantitative Association Rules in College Employment Information Data[J].,2013,(11):199-202.
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量化关联规则在高校就业信息数据中的应用()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年11期
页码:
199-202
栏目:
应用开发研究
出版日期:
1900-01-01

文章信息/Info

Title:
Application of Quantitative Association Rules in College Employment Information Data
文章编号:
1673-629X（2013）11-0199-04
作者:
张晓萍1; 朱玉全1; 陈耿2
[1]江苏大学 计算机科学与通信工程学院;[2]南京审计学院 信息科学学院
Author(s):
ZHANG Xiao-ping[1]; ZHU Yu-quan[1]; CHEN Geng[2]
关键词:
数据挖掘; 量化关联规则; k-means聚类算法; 就业信息
Keywords:
data mining; quantitative association rules; k-means clustering algorithm; employment information
文献标志码:
A
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
Abstract:
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

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更新日期/Last Update: 1900-01-01