基于FP-Tree有效挖掘最大频繁项集  

Efficiently Mining of Maximal Frequent Item Sets Based on FP-Tree

作　　者：颜跃进[1] 李舟军[1] 陈火旺[1] 

机构地区：[1]国防科学技术大学计算机学院,湖南长沙410073

出　　处：《软件学报》2005年第2期215-222,共8页Journal of Software

基　　金：国家自然科学基金;国家高技术研究发展计划(863)~~

摘　　要：最大频繁项集的挖掘过程中,在最小支持度较小的情况下,超集检测是算法的主要耗时操作.提出了最大频繁项集挖掘算法 FPMFI(frequent pattern tree for maximal frequent item set)使用基于投影进行超集检测的机制,有效地缩减了超集检测的时间.另外,算法FPMFI通过删除FP子树(conditional frequent pattern tree)的冗余信息,有效地压缩了 FP 子树的规模,减少了遍历的开销.分析表明,算法 FPMFI 具有优越性.实验比较说明,在最小支持度较小时,算法 FPMFI 的性能优于同类算法 1 倍以上.During the process of mining maximal frequent item sets, when minimum support is little, superset checking is a kind of time-consuming and frequent operation in the mining algorithm. In this paper, a new algorithm FPMFI (frequent pattern tree for maximal frequent item sets) for mining maximal frequent item sets is proposed. It adopts a new superset checking method based on projection of the maximal frequent item sets, which efficiently reduces the cost of superset checking. In addition, FPMFI also compresses the conditional FP-Tree (frequent pattern tree) greatly by deleting the redundant information, which can reduce the cost of accessing the tree. It is proved by theoretical analysis that FPMFI has superiority and it is revealed by experimental comparison that the performance of FPMFI is superior to that of the similar algorithm based on FP-Tree more than one time.

关 键 词：最大频繁项集 频繁模式树 超集检测 最大频繁项集投影 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]