首页> 外文期刊>Cybernetics and information technologies: CIT >An Optimization of Closed Frequent Subgraph Mining Algorithm
【24h】

An Optimization of Closed Frequent Subgraph Mining Algorithm

机译:封闭频繁子图挖掘算法的优化

获取原文
           

摘要

Graph mining is a major area of interest within the field of data mining in recent years. A key aspect of graph mining is frequent subgraph mining. Central to the entire discipline of frequent subgraph mining is the concept of subgraph isomorphism. One major issue in early subgraph isomorphism research concerns computational complexity. Normally, the subgraph isomorphism problem is NP-complete. Previous studies of frequent subgraph mining have not solved NP-complete problem in the subgraph isomorphism. In this paper, we propose a new algorithm which can deal with this problem. The proposed algorithm can solve the subgraph isomorphism in polynomial time in some settings. Moreover, the new algorithm is proved theoretically more effective than previous studies in closed frequent subgraph mining.
机译:近年来,图挖掘是数据挖掘领域中的一个主要关注领域。图挖掘的关键方面是频繁的子图挖掘。子图同构概念是频繁子图挖掘整个学科的核心。早期子图同构研究的一个主要问题是计算复杂性。通常,子图同构问题是NP完全的。以前的频繁子图挖掘研究尚未解决子图同构中的NP完全问题。在本文中,我们提出了一种可以解决该问题的新算法。所提算法在某些设置下可以解决多项式时间内的子图同构。此外,在封闭频繁子图挖掘中,新算法在理论上比以前的研究更有效。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号