一种挖掘最大频繁集的算法  

An Algorithm for Mining Maximum Frequent Itemsets

作　　者：李清峰[1] 王莉[1] 周伟林[1] 陈火旺[2] 

机构地区：[1]湖南商学院计算机与电子工程系,长沙410205 [2]国防科学技术大学计算机学院,长沙410073

出　　处：《模式识别与人工智能》2007年第5期661-666,共6页Pattern Recognition and Artificial Intelligence

基　　金：湖南省自然科学基金(No.04JJ40048);湖南省教育厅科研课题基金(No.05C545)

摘　　要：挖掘频繁项目集是关联规则应用中的关键问题.目前挖掘频繁集主要有 Apriori 算法和频繁树法两大类.本文提出与上述两类算法完全不同的高效挖掘最大频繁集的算法:最小支持数最小组合算法(MSMCA).该算法不产生候选频繁集,能较大减少计算量的开销.此外,在此算法的研究中,本文提出另一个子课题:重复数列中最小支持数最小组合算法研究.Mining complete set of frequent patterns remains a key problem to the application of association rules. Up to date, the most commonly used methods are Apriori algorithm and FP-TREE algorithm. In this paper, a high efficient algorithm, minimal support minimal combination algorithm （MSMCA）, is proposed. It is completely different from the two existing methods. The candidate set of frequent itemsets are not produced by using MSMCA, thus the cost of computer reduces largely. In addition, a sub-project, minimal support minimal combination in repeat array, is proposed in the course of studying MSMCA.

关 键 词：关联规则 最大频繁集 最小支持数最小组合算法(MSMCA) 重复数列中最小支持数最小组合(MSMCRA) 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]