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Understanding the Crucial Role of Attribute Interaction in Data Mining

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Abstract

This is a review paper, whose goal is tosignificantly improve our understanding of thecrucial role of attribute interaction in datamining. The main contributions of this paperare as follows. Firstly, we show that theconcept of attribute interaction has a crucialrole across different kinds of problem in datamining, such as attribute construction, copingwith small disjuncts, induction of first-orderlogic rules, detection of Simpson's paradox,and finding several types of interesting rules.Hence, a better understanding of attributeinteraction can lead to a better understandingof the relationship between these kinds ofproblems, which are usually studied separatelyfrom each other. Secondly, we draw attention tothe fact that most rule induction algorithmsare based on a greedy search which does notcope well with the problem of attributeinteraction, and point out some alternativekinds of rule discovery methods which tend tocope better with this problem. Thirdly, wediscussed several algorithms and methods fordiscovering interesting knowledge that,implicitly or explicitly, are based on theconcept of attribute interaction.

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  1. Postgraduate Program in Computer Science, Pontificia Universidade Catolica do Parana, Rua Imaculada Conceicao, 1155, Curitiba, PR, 80215-901, Brazil (E-mail

    Alex A. Freitas

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Freitas, A.A. Understanding the Crucial Role of Attribute Interaction in Data Mining. Artificial Intelligence Review 16, 177–199 (2001). https://doi.org/10.1023/A:1011996210207

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