Reducing redundancy in characteristic rule discovery by using integer programming techniques

The discovery of characteristic rules is a well-known data mining task and has lead to several successful applications. However, because of the descriptive nature of characteristic rules, typically a (very) large number of them is discovered during the mining stage. This makes monitoring and control of these rules, in practice, extremely costly and difficult. Therefore, a selection of the most promising subset of rules is desirable. Some heuristic rule selection methods have been proposed in the literature that deal with this issue. In this paper, we propose an integer programming model to solve the problem of optimally selecting the most promising subset of characteristic rules. Moreover, the proposed technique enables to control a user-defined level of overall quality of the model in combination with a maximum reduction of the redundancy extant in the original ruleset. We use real-world data to empirically evaluate the benefits and performance of the proposed technique against the we...

• Type: Journal
• Year: 2000
• Where: IDA
• Authors: Tom Brijs, Koen Vanhoof, Geert Wets
• Keywords: Characteristic Rules, IDA 2000, Information Technology, Integer Programming, Promising Subset