基于关联图的加权关联规则挖掘算法  

Weighted Association Rules Mining Algorithm Based on Association Graph

作　　者：陈文[1] 

机构地区：[1]铜陵学院数学与计算机科学系,铜陵244000

出　　处：《计算机工程》2010年第13期59-61,共3页Computer Engineering

基　　金：安徽高校省级优秀青年人才基金资助项目(2010SQRL170);安徽省教育厅自然科学研究基金资助重点项目(2006KJ027A);安徽省自然科学基金资助项目(070412055)

摘　　要：针对交易数据库中数据项重要性不同的现象,引入加权支持度和最小支持期望的概念,提出一种基于关联图的加权关联规则模型,并在该模型基础上,设计了改进的加权关联规则挖掘算法。该算法扫描数据库仅一次,采用关联图存储频繁2项集信息,通过构建基于图的剪枝策略,减少验证频繁项集的计算量,有效提高加权频繁项集的生成效率。By introducing the concept of weighted support and minimum support expect,a new model of weighted association rule mining is presented in order to solve the problem that items have not the same importance in datasets.Based on the model,a new improved algorithm for mining weighted association rules based on association graph is proposed.The algorithm only scans the database once,stores the frequent 2 itemsets with association graph,and builds an effective pruning strategy to reduce the computation.It improves the efficiency of weighted frequent itemsets generation.

关 键 词：加权关联规则 最小支持期望 关联图 

分 类 号：TP311.52[自动化与计算机技术—计算机软件与理论]