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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9101

Title: ARUBAS: An Association Rule Based Similarity Framework for Associative Classifiers
Authors: VANHOOF, Koen
WETS, Geert
Issue Date: 2008
Publisher: IEEE
Citation: Bonchi, Francesco & Berendt, Bettina & Giannotti, Fosca & Gunopulos, Dimitrios & Turini, Franco & Zaniolo, Carlo & Ramakrishnan, Naren & Wu, Xindong (Ed.) Proceedings of Eight IEEE International Conference on Data Mining Workshops: vol. 8. p. 692-699.
Abstract: This article introduces ARUBAS, a new framework to build associative classifiers. In contrast with many existing associative classifiers, it uses class association rules to transform the feature space and uses instance-based reasoning to classify new instances. The framework allows the researcher to use any association rule mining algorithm to produce the class association rules. Every aspect of the framework is extensively introduced and discussed and five different fitness measures used for classification purposes are defined. The empirical results determine which fitness measure is the best and compares the framework with other classifiers. These results show that the ARUBAS framework is able to produce associative classifiers which are competitive with other classification techniques. More specifically, with ARUBAS-Scheffer-phi5 we have introduced a parameter-free algorithm which is competitive with classification techniques such as C4.5, RIPPER and CBA.
URI: http://hdl.handle.net/1942/9101
DOI: 10.1109/ICDMW.2008.58
ISBN: 978-0-7695-3503-6
Category: C1
Type: Proceedings Paper
Appears in Collections: Research publications

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