多最小支持度下的关联规则及其挖掘方法研究

数据挖掘指的是从大量的数据中提取隐含的、事先未知的、并且潜在有用的知识技术，是目前国际上数据库和信息决策领域最前沿的研究方向之一。关联规则是当前数据挖掘研究的主要领域之一，获取具有更高价值的规则是该领域的一个研究重点。针对目前大多数挖掘算法只能发现单一支持度下的关联规则问题，文中提出了一种基于多支持度的挖掘策略及在原有AprioriTid算法基础上的改进算法。

Association rules and an approach of mining with multiple minimum supports

Data mining refers to extracting implicit, previously unknown and usable knowledge from large amounts of data. It is one of the frontiers of research in the fields of database and DSS. Association rules is one of the main research fields in data mining, and to gain more valuable rules is a focus in this field. But most of the most current algorithms earl only find out the rules with only one support. To solve this problem, the paper puts out a data mining stratagem based on multiple minimum supports and an improving way on basis of AprioriTid algorithm.