Efficient Strategies for Mining Negative Association Rules

挖掘负关联规则的有效策略

• 批准号: DP0449535
• 负责人: Prof Chengqi Zhang
• 金额: $ 8.98万
• 依托单位: University of Technology Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2004
• 资助国家: 澳大利亚
• 起止时间: 2004-04-01 至 2007-06-30
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0449535
• 关键词: Efficient; Strategies; Mining; Negative; Association

Negative association rules (NAR) catch mutually-exclusive correlations among items. They play important roles just as traditional association rules (TAR) do. For example, in stock market surveillance based on alert logs, NARs detect which alerts are false. There are essential differences between mining TARs and NARs because NARs are hidden in infrequent itemsets. This research will develop efficient strategies for mining NARs in databases. These strategies are expected to be about ten times faster than existing ones. This project will deliver database-independent and high-performance mining algorithms for decision-making. The results can benefit Australian marketing and financial companies as well as health and security departments for smart information use.

负关联规则（NAR）捕获互斥相关性 在项目之间。它们和传统的联想一样， 规则（TAR）。例如，在基于警报日志的股票市场监视中，NAR检测哪些警报是假的。TAR和NAR挖掘有本质的区别，因为NAR隐藏在非频繁项集中。这项研究将开发有效的策略，挖掘数据库中的NAR。这些策略预计将比现有策略快10倍左右。该项目将为决策提供独立于数据库的高性能挖掘算法。研究结果可以使澳大利亚营销和金融公司以及卫生和安全部门受益，以实现智能信息使用。