标题： 新的关联规则的概率兴趣度量 显示英文标题

标题： New probabilistic interest measures for association rules

摘要： 挖掘关联规则是发现事务数据库中有意义模式的重要技术。 已经提出了许多有趣的度量方法用于关联规则。 然而，这些度量方法未能考虑挖掘数据的概率特性。 在本文中，我们首先介绍一个简单的事务数据概率框架，该框架可用于在没有关联的情况下模拟事务数据。 我们使用此类数据和来自杂货店的实际数据库来探索置信度和提升度的行为，这两个是用于规则挖掘的流行兴趣度量。 结果表明，置信度受规则左侧项目频率的系统影响，而提升度在过滤事务数据中的随机噪声方面表现不佳。 基于我们开发的概率框架，我们提出了两种新的兴趣度量方法，超提升度和超置信度，可用于过滤或排序挖掘出的关联规则。 对于虚假规则有问题的应用，新度量方法表现出明显优于提升度的性能。 显示英文摘要

摘要： Mining association rules is an important technique for discovering meaningful patterns in transaction databases. Many different measures of interestingness have been proposed for association rules. However, these measures fail to take the probabilistic properties of the mined data into account. In this paper, we start with presenting a simple probabilistic framework for transaction data which can be used to simulate transaction data when no associations are present. We use such data and a real-world database from a grocery outlet to explore the behavior of confidence and lift, two popular interest measures used for rule mining. The results show that confidence is systematically influenced by the frequency of the items in the left hand side of rules and that lift performs poorly to filter random noise in transaction data. Based on the probabilistic framework we develop two new interest measures, hyper-lift and hyper-confidence, which can be used to filter or order mined association rules. The new measures show significantly better performance than lift for applications where spurious rules are problematic.