灵活和可行的支持措施,用于挖掘频繁模式的大标签图表
1Department of Computer Science & Engineering, University of South Florida.
概括
本研究引入了用于图形挖掘的新超图框架,统一了现有的支持措施,并提出了新的最小实例 (MI) 和最小顶点覆盖 (MVC) 措施,以在单个图形中更有效地发现频繁的模式.
科学领域:
- 计算机科学 计算机科学
- 数据挖掘 数据挖掘
- 图形理论 图形理论
背景情况:
- 图形数据库对于信息表示越来越受欢迎.
- 在单个图表中频繁采集模式采矿面临着支持措施和搜索计划的挑战.
研究的目的:
- 提出一个新的框架,用于构建单图矿业的支持措施.
- 引入新的支持措施,结合现有方法的优势.
- 在一个单一的理论框架内统一各种支持措施.
主要方法:
- 开发了一个基于事件/实例超图的框架.
- 引入了最低实例 (MI) 和最低顶部覆盖 (MVC) 支持措施.
- 为基于超图的测量提供了多项式时间放松和边界定理.
主要成果:
- 拟议的框架统一了现有和新的支持措施.
- 该MI测量是线性时间可计算的,并近似的实例数.
- 虽然MVC测量是NP-hard,但在多项式时间中是可近似的.
结论:
- 基于超图的框架提供了一种统一而灵活的方法来定义和分析图形采矿的支持措施.
- 新的MI和MVC措施为频繁的模式发现提供了有效的替代方案.
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