Constraint association-rule mining algorithm based on transaction clip
一种基于事务修剪的约束关联规则的挖掘算法

Aiming at a familiar and simple constraint that some items must or must not present in rules,a fast clipped-transaction-based constraint association-rule mining algorithm was put forward.This algorithm firstly scanned data base to clip transactions horizontally and vertically,then mined frequent item sets from clipped data set to form rules' candidate head sets,body sets and rule item sets.Finally,it scanned original data base again to gain association rules according to minimum confidence constraint.Experiments show that,compared with common strategy of succinct constraint,this algorithm has better performance.