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基于频繁模式树的约束最大频繁项目集挖掘算法研究
引用本文:陈耿,朱玉全,宋余庆,陆介平,孙志挥.基于频繁模式树的约束最大频繁项目集挖掘算法研究[J].应用科学学报,2006,24(1):64-69.
作者姓名:陈耿  朱玉全  宋余庆  陆介平  孙志挥
作者单位:1. 东南大学计算机科学与工程系,江苏,南京,210096;南京审计学院,江苏,南京,210029
2. 江苏大学计算机科学与通信工程学院,江苏,镇江,212013
3. 东南大学计算机科学与工程系,江苏,南京,210096
基金项目:中国科学院资助项目;江苏大学校科研和教改项目
摘    要:目前绝大多数频繁项目集(或最大频繁项目集)挖掘算法并没有考虑相关领域知识,其结果会产生许多无关的模式.因此,发现约束频繁(或约束最大频繁)项目集是多种数据挖掘应用中的关键问题,然而,这方面的研究工作却很少.为此该文提出了一种快速的基于频繁模式树(FP-tree:一种扩展前缀树结构)的约束最大频繁项目集挖掘及其更新算法.实验结果表明该算法是快速有效的.

关 键 词:关联规则  项约束  最大频繁项目集  频繁模式树  增量式更新
文章编号:0255-8297(2006)01-0064-06
收稿时间:2004-09-27
修稿时间:2004-09-272004-12-14

Algorithm for Mining Constrained Maximum Frequent Itemsets Based on Frequent Pattern Tree
CHEN Geng,ZHU Yu-quan,SONG Yu-qing,LU Jie-ping,SUN Zhi-hui.Algorithm for Mining Constrained Maximum Frequent Itemsets Based on Frequent Pattern Tree[J].Journal of Applied Sciences,2006,24(1):64-69.
Authors:CHEN Geng  ZHU Yu-quan  SONG Yu-qing  LU Jie-ping  SUN Zhi-hui
Institution:1. Department of Computer and Engineering, Southeast University, Nanfing 210096, China ; 2. School of Computer Science and Communication Engineering, Jiangsu University, Zhenfiang 212013, China ; 3. Nanjing Audit University, Nanjing 210029, China
Abstract:Most algorithms of frequent itemsets(or maximum frequent itemsets) do not consider any domain knowledge.As a result they generate many irrelevant patterns.Therefore,finding constrained maximum frequent itemsets is a key in important data mining application such as discovery of constrained association rules,constrained strong rules,etc.Little work has been done on this problem. This paper presents an effective algorithm for mining constrained maximum frequent itemsets and its update,update constrained maximum frequent itemsets algorithm,based on a novel frequent pattern tree(FP-tree) structure that is an extended prefix-tree structure for storing compressed and crucial information about frequent patterns.Experiments show that the algorithm is effective.
Keywords:association rules  item constraint  maximum frequent itemsets  frequent pattern tree  incremental updating
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