Full Citation

Title: Pruning Derivative Partial Rules During Impact Rule Discovery

Citation Type: Conference Paper

Publication Year: 2005

ISBN: 3-540-26076-5, 978-3-540-26076-9

ISSN:

DOI: 10.1007/11430919_10

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PMID:

Abstract: Because exploratory rule discovery works with data that is only a sample of the phenomena to be investigated, some resulting rules may appear interesting only by chance. Techniques are developed for automatically discarding statistically insignificant exploratory rules that cannot survive a hypothesis with regard to its ancestors. We call such insignificant rules derivative extended rules. In this paper, we argue that there is another type of derivative exploratory rules, which is derivative with regard to their children. We also argue that considerable amount of such derivative partial rules can not be successfully removed using existing rule pruning techniques. We propose a new technique to address this problem. Experiments are done in impact rule discovery to evaluate the effect of this derivative partial rule filter. Results show that the inherent problem of too many resulting rules in exploratory rule discovery is alleviated.

Url: http://link.springer.com/10.1007/11430919_10

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Authors: Huang, Shiying; Webb, Geoffrey I.

Conference Name: Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining

Publisher Location: Hanoi, Vietnam

Data Collections: IPUMS USA

Topics: Methodology and Data Collection

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