中文摘要：

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。

英文摘要：

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.