Understanding the crucial role of attribute interaction in data mining

Freitas, Alex A. (2001) Understanding the crucial role of attribute interaction in data mining. Artificial Intelligence Review, 16 (3). pp. 177-199. ISSN 0269-2821. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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This is a review paper, whose goal is to significantly improve our understanding of the crucial role of attribute interaction in data mining. The main contributions of this paper are as follows. Firstly, we show that the concept of attribute interaction has a crucial role across different kinds of problem in data mining, such as attribute construction, coping with small disjuncts, induction of first-order logic rules, detection of Simpson's paradox, and finding several types of interesting rules. Hence, a better understanding of attribute interaction can lead to a better understanding of the relationship between these kinds of problems, which are usually studied separately from each other. Secondly, we draw attention to the fact that most rule induction algorithms are based on a greedy search which does not cope well with the problem of attribute interaction, and point out some alternative kinds of rule discovery methods which tend to cope better with this problem. Thirdly, we discussed several algorithms and methods for discovering interesting knowledge that, implicitly or explicitly, are based on the concept of attribute interaction.

Item Type: Article
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 17:58
Last Modified: 20 May 2014 08:20
Resource URI: https://kar.kent.ac.uk/id/eprint/13524 (The current URI for this page, for reference purposes)
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