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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/6448

Title: Reducing redundancy in characteristic rule discovery by using integer programming techniques
Authors: BRIJS, Tom
WETS, Geert
Issue Date: 2000
Publisher: IOS Press
Citation: Journal of intelligent data analysis, 4. p. 229-240
Abstract: The discovery of characteristic rules is a well-known data mining task and has lead to several succesful 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 well-known RuleCover heuristic. Results demonstrate that the proposed integer programming techniques are able to significantly reduce the number of retained rules and the level of redundancy in the final ruleset. Moreover, the results demonstrate that the overall quality in terms of the discriminant power of the final ruleset slightly increases if integer programming methods are used.
URI: http://hdl.handle.net/1942/6448
ISSN: 1088-467X
Category: A1
Type: Journal Contribution
Validation: vabb, 2010
Appears in Collections: Research publications

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