Extending association rule discovery to numeric data

这个项目解决了关联规则发现的一个关键限制，这是数据挖掘中使用的主要技术之一。许多有价值的数据都是数字。然而，关联规则发现不能很好地对数值数据进行建模，这一局限性极大地制约了关联规则发现的应用。该项目研究了一种克服这一限制的新技术。影响规则发现查找与数字分布的关联。这使得数据分析师能够精确地发现他们通常从数字数据中寻找的信息类型，例如，如何最大化健康、合规性、利润或准确性等结果的平均或汇总度量。

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.