计算机工程与应用Issue(3)：112-116,5.DOI:10.3778/j.issn.1002-8331.1303-0289
挖掘不确定频繁子图的改进算法的研究
Research of improved mining frequent subgraph patterns in uncertain graph databases
胡健 1何林波 2毛伊敏 1杨健2
作者信息
- 1. 江西理工大学 应用科学学院，江西 赣州 341000
- 2. 江西理工大学 信息工程学院，江西 赣州 341000
- 折叠
摘要
Abstract
Mining frequent subgraph patterns in graph databases is a popular and important problem which has wide applica-tion in lots of domains. At the moment, the MUSE is a typical algorithm for graph mining, but its expected supports computa-tion costs a lot and the time efficiency is so low. So this paper proposes a method that combines classification thought with BFS thought to find frequent subgraph patterns(EDFS). It uses improved GSpan algorithm to deal with uncertain graphs to reduce the space of subgraph patterns, then integrates classification thought with BFS thought to mine frequent subgraph patterns. The subgraph isomorphism tests and the edge whether existing tests indicate that EDFS is more efficient than MUSE.关键词
不确定图/图挖掘/频繁子图集/划分思想/混合策略Key words
uncertain graph/graph mining/frequent subgraph patterns/classification thought/mixed algorithm分类
信息技术与安全科学引用本文复制引用
胡健,何林波,毛伊敏,杨健..挖掘不确定频繁子图的改进算法的研究[J].计算机工程与应用,2015,(3):112-116,5.
