摘要： 在经典Apriori 算法的基础上，提出了一种考虑了时间因素的股票联动关联规则挖掘算法。该算法首先对股票原始数据采用滑动时间窗口技术进行了预处理，得到了适合挖掘的事务集；然后使用SQL 语言详细描述了关联规则的生成过程。根据证券行业的实际情况，采用了元规则指导的挖掘方法，从而使挖掘结果聚焦于投资者感兴趣的规则形式上，并且也提高了挖掘过程的效率。

关键词: 数据挖掘；关联规则；元规则；股票；证券；知识发现

Abstract: The paper presents a new algorithm that embodies the factor of time for mining association rules in stock linkage information based on Apriori algorithm. Firstly, it gets the transaction sets which are suitable for mining rules after preprocess the original stock data by adopting the sliding-time- window technology; afterward, detailedly discusses the procedure to construct the association rules by using SQL. Secondly, it adopts the mining approach guided by meta-rule according to the securities industry in practice, which can make the mining results focus on the model of target rules that the investors are interested in and improve the efficiency of mining process

Key words: Data mining; Association rules; Meta-rule; Stock; Securities; KDD