摘要
运用图论中的完全图知识 ,对关联规则提取过程的第一阶段进行改造 ,把大项集计算转换为集合的并和交两种基本运算 ,并利用候选大项集生成过程中的中间结果对已知大项集进行过滤 ,大大减少不必要的重复计算 。
Based on theory of complete graph, an improved algorithm for mining association rules is given in this paper. Large item computing, the first phase of Apriori Algorithm, is realized just by basic computing-the union and minus of set. And at the same time, to improve the speed of generation of large itemsets, several efficient ways are introduced to filter the meta result of large items.
出处
《小型微型计算机系统》
CSCD
北大核心
2003年第7期1343-1345,共3页
Journal of Chinese Computer Systems
关键词
数据挖掘
关联规则
大项集
完全图
data mining
association rule
large itemsets
complete graph
