OLAP中基于FP-增长的关联规则挖掘

关联规则挖掘是一种发现属性问关系的方法，主要用于在商务事务记录中挖掘事务问关系。本文将已经广泛使用的FP-增长(frequent-pattern growth，频繁模式增长)算法进行改进，实现了OLAP中的关联规则挖掘。改进算法分别针对单维、多维、混合维三种关联规则，将多维立方体转化成不同的关系表，通过关系表产生关联规则．并利用立方体中的事实值作为进一步约束，生成了更有价值的规则。

Mining of Association Rules on the Basis of FP-Growth in OLAP

Mining of association rules is a method to find the relation among the attributes. It is mainly used to find the relations of transactions in the business transaction records. This paper realizes the mining of association rules in OLAP by improving the FP-Growth algorithm which is widely used. The improved algorithm converses the cube into the different relation tables according to the types of association rules, intra dimensional rules. inter dimensional rules and hybrid dimensional rules. And it generates association rules from the relation tables. This paper also introduces the method of generating more interesting rules constrained by the factual value in the cube.