一种基于组合方式改进的频繁项集挖掘算法  

An Improved Frequent Itemsets Mining Algorithm Based on Combination Approach

作　　者：汪成亮[1,2] 罗昌银[1] 

机构地区：[1]重庆大学计算机学院,重庆400044 [2]重庆大学电气工程学院,重庆400044

出　　处：《计算机系统应用》2010年第1期67-71,共5页Computer Systems & Applications

基　　金：重庆市自然科学基金(CSTC;2007BB6118);中国博士后科学基金(20080430750)

摘　　要：FP-growth算法用于关联规则挖掘分成两个阶段:构建频繁模式树和进行频繁模式挖掘;对这两个阶段分别进行改进,若项头表中存在同频度的频繁项,在构建FP-tree的过程动态调整其位置,构建压缩的最优化FP-tree,提出了IMFP-tree算法。在进行频繁模式挖掘阶段,提出CFP-mine算法,CFP-mine算法采用一种新方法构建条件模式基,且采用组合方式挖掘频繁项集,有别于传统FP-growth算法的挖掘过程,理论上证明和实验验证本算法的正确性和高效性。FP-growth algorithm for mining association rules is divided into two phases：building a FP-tree and mining frequent patterns.In this paper new algorithms are proposed to improve the two stages separately.In the first stage,if frequent items in header table have the same support,their position can be dynamically changed to construct a compressed and optimized FP-tree.IMFP-tree algorithm is proposed to realize that function.In the second stage,CFP-mine algorithm is proposed,which constructs pattern-base by using a new method different from the conditional pattern-base in FP-growth.This paper mines frequent itemsets with a new combination method without recursive construction of conditional FP-tree.It has theoretically proved and experimentally verified the correctness and efficiency of CFP-mine algorithm.

关 键 词：关联规则 FP树 频繁项集 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TU984.265[自动化与计算机技术—计算机科学与技术]