计算机工程与应用2011，Vol.47Issue(10)：113-117,5.DOI:10.3778/j.issn.1002-8331.2011.10.032
Apriori算法用于频繁子图挖掘的改进方法
Improved method based on Apriori-based frequent sub-graph mining algorithm.
摘要
Abstract
AGM(Apriori-based Graph Mining) algorithm is the first one to put the Apriori idea into the use of frequent sub-graph mining. This algorithm is simple and based on recursion statistics. But graph data set is very large and sub-graph isomorphism problem is available, when candidate subgraphs are generated and so many redundant sub-graphs would be generated, which makes the high cost in computing time. An improved method based on AGM is proposed to get the reduction of redundant sub-graphs and make the new algorithm more efficient in computing time,compared to AGM algorithm. This paper examines the computing time for various minimum support, the result of which proves that the improved algorithm cuts down the computing time,compared to AGM algorithm,improving the efficiency of frequent sub-graph mining.关键词
频繁子图挖掘/AGM算法/子图同构Key words
frequent sub-graph mining/Apriori-based Graph Mining(AGM) algorithm/sub-graph isomorphism分类
信息技术与安全科学引用本文复制引用
陈立宁,罗可..Apriori算法用于频繁子图挖掘的改进方法[J].计算机工程与应用,2011,47(10):113-117,5.基金项目
国家自然科学基金(the National Natural Science Foundation of China under Grant No.10926189,No.10871031) （the National Natural Science Foundation of China under Grant No.10926189,No.10871031）
湖南省科技计划项目(No.2008FJ3015). （No.2008FJ3015）
