Extending association rule discovery to numeric data

将关联规则发现扩展到数值数据

• 批准号: DP0450219
• 负责人: Prof Geoffrey Webb
• 金额: $ 10.45万
• 依托单位: Monash University
• 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: 2004-02-24 至 2006-12-22
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP0450219
• 关键词: Extending; association; rule; discovery; numeric

This project tackles a key limitation of association-rule discovery, which is one of the main techniques used in data mining. Much valuable data is numeric. However, association-rule discovery cannot satisfactorily model numeric data, a limitation that has greatly restricted its application. This project investigates a novel new technique that overcomes this limitation. Impact-rule discovery finds associations with numeric distributions. This allows data analysts to discover precisely the type of information that they usually seek from numeric data, for example, how to maximize either average or aggregate measures of outcomes such as health, compliance, profit, or accuracy.

这个项目解决了关联规则发现的一个关键限制，关联规则发现是数据挖掘中使用的主要技术之一。 许多有价值的数据是数字。 然而，关联规则发现不能令人满意地模拟数值型数据，这一局限性极大地限制了其应用。 该项目研究了一种克服这一限制的新技术。 影响规则发现发现与数字分布的关联。 这使得数据分析师能够准确地发现他们通常从数字数据中寻找的信息类型，例如，如何最大限度地提高健康，合规性，利润或准确性等结果的平均或汇总指标。