在大型标记图中计数频繁模式:基于超图的方法
Jinghan Meng1, Napath Pitaksirianan1, Yi-Cheng Tu1
1University of South Florida, 4202 E Fowler Ave, Tampa, FL 33620, USA.
概括
本研究引入了一个新的超图框架,以统一图形挖掘支持措施,开发新的最小实例 (MI) 和最小顶点覆盖 (MVC) 措施. 这项研究揭示了多项式时间中MVC和最大独立集合 (MIS) 的常数因子近似算法.
科学领域:
- 图形数据库和数据挖掘
- 超图理论和算法 超图理论和算法
- 计算复杂性和近似算法算法.
背景情况:
- 图形数据库对于信息表示越来越受欢迎.
- 在单个图表中频繁的模式挖掘在支持措施和搜索计划中面临挑战.
- 现有的支持措施,如基于最小图像和基于重叠图的支持措施,都有局限性.
研究的目的:
- 提出一个新的框架,用于设计支持措施在单图矿业.
- 引入新的支持措施,最小实例 (MI) 和最小顶点覆盖 (MVC),基于事件/实例超图.
- 统一和分析现有和新的支持措施,揭示它们的界限关系和硬度特性.
主要方法:
- 开发了一个基于事件/实例超图的框架,以统一支持措施.
- 介绍并分析了新的支持措施:最小实例 (MI) 和最小顶点覆盖 (MVC).
- 利用线性编程和半确定的编程来实现MVC和最大独立集合 (MIS) 的多项式时间放松.
主要成果:
- 拟议的框架统一了大多数主要的现有和新的支持措施.
- 最小实例 (MI) 测量被证明与模式实例的数量密切相关.
- 发现了MVC和MIS的常数近似算法,挑战了先前的假设.
- 开发了MVC和MIS的多项式时间放松,计数限制在一个常数因子内.
结论:
- 基于超图的框架有效地统一了图形挖掘的各种支持措施.
- 像MI和MVC这样的新措施提供了优势,并在框架内得到了很好的描述.
- 该研究提供了关于图形挖掘措施的近似性和复杂性的重要见解.
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