基于粗糙集和单事务项组合的关联规则挖掘算法  

Algorithm of Mining Association Rules Based on Rough Sets and Transaction Itemsets Combination

作　　者：王明芳[1] 蒋芸[1] 王勇[2] 明利特[1] 周涛[1] 周泽寻[1] 

机构地区：[1]西北师范大学数学与信息科学学院,甘肃730070 [2]西北工业大学计算机学院,西安710072

出　　处：《计算机科学》2011年第11期234-238,共5页Computer Science

基　　金：国家自然科学(60873196);甘肃省科技计划(甘肃省自然科学基金项目1010RJZA022);西北师范大学2010年第三期知识与创新工程科研骨干项目(nwnu-kjcxgc-03-67);西北师范大学2006-2010年度重点学科"网络计算"资助

摘　　要：Apriori算法必须反复地扫描数据库才能求出频繁项集,效率较低,且不支持更新挖掘。为了解决这些问题,提出了一种基于粗糙集、单事务项组合和集合运算的关联规则挖掘算法。本算法首先利用粗糙集进行属性约简,对新决策表中的每个事务进行"数据项"组合并标记地址,然后利用集合运算的方法计算支持度和置信度即可挖掘出有效规则。本算法只需要一次扫描数据库,同时有效地支持了关联规则的更新挖掘。应用实例和实验结果表明,本算法明显优于Apriori算法,是一种有效且快速的关联规则挖掘算法。The Apriori algorithm contains weaknesses such as often requiring a large number of repeated passes over the database to generate the frequent item sets and does not support the incremental updating.To solve these problems,a novel algorithm was proposed in this paper which is based on rough sets,single transaction combination itemsets and set operations for mining.It firstly uses the rough sets to reduce attributes,and then combines data item to each itemset from new decision table and marks it＇s tags.Finally,it calculates the support and confidence using set operations.This novel algorithm just needs to scanning the decision table only once,while effectively supporting the update of association rules mining.The results of application and experiments show that this novel algorithm is better than Apriori algorithm,it is an effective and fast algorithm for mining association rules.

关 键 词：粗糙集 单事务项组合 集合运算 更新挖掘 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]