基于频繁闭项目集的关联规则挖掘算法  

An Efficient Algorithm for Mining Association Rules Based on Frequent Closed Item Sets

作　　者：朱玉文[1] 陈陵涛[1] 刘万春[1] 贾云得[1] 

机构地区：[1]北京理工大学信息科学技术学院计算机科学工程系,北京100081

出　　处：《北京理工大学学报》2003年第3期345-349,共5页Transactions of Beijing Institute of Technology

基　　金：国家部委基金资助项目(GJ2-2)

摘　　要：提出一种有效的基于频繁闭项目集的关联规则挖掘算法RIFCI.该算法采用挖掘频繁项目闭集取代传统的频繁项目集,同时在项目集和事务集中展开搜索.通过对UCI机器学习库中10个数据集的测试,与工业标准C4.5比较,错误率低于19.48%,在准确度不变的情况下,生成规则数目低于传统算法,提高了算法的效率.An efficient algorithm, RIFCI, for mining association rule, is proposed by simultaneously mining the frequent closed item sets instead of traditional frequent item sets and at the mean time exploring both the item set space and transaction space, as is distinguished from all previous association mining methods that exploit only the item set search space. Compared with the industrial standard C4\^5, the error rate of the result is less than 19\^48%, got by searching ten data sets in UCI repository of machine learning database. This algorithm, without changing the accuracy, generates the number of roles lower than previous methods, which improves the efficiency.

关 键 词：知识发现 关联规则挖掘 频繁项目集 频繁闭项目集 

分 类 号：TP311.131[自动化与计算机技术—计算机软件与理论]