改进的Fp-Growth数据关联挖掘算法研究

Improved Fp-Growth Data Association Mining Algorithm

改进后的Fp-Growth挖掘算法适用于对大型数据库的数据关联规则的挖掘,基于一种新的数据库分隔方法来分隔数据库,并对分隔得到的各数据库子集用算法进行约束频繁项集挖掘。改进的数据库划分策略克服了占用内存大的缺陷,提高了挖掘速度,实时性更强。

This paper proposes an improved Fp-Growth algorithm,the improved Fp-Growth algorithm applied to a large database of data mining of association rules,based on a new database partition method to separate the database,and to get the database subset separated with algorithm for constrained frequent item-sets mining.The improved database partitioning strategy overcomes the defect of using too much memory,enhances the speed of mining,and strengthens the practicability.