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Discovering surprising patterns by detecting occurrences of Simpson's paradox

Fabris, Carem C., Freitas, Alex A. (2000) Discovering surprising patterns by detecting occurrences of Simpson's paradox. In: Bramer, Max and Macintosh, Ann and Coenen, Frans, eds. Research and Development in Intelligent Systems XVI. B C S Conference Series . pp. 148-160. Springer-Verlag, Berlin ISBN 1-85233-231-X. (doi:10.1007/978-1-4471-0745-3_10) (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) (KAR id:21712)

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.
Official URL:
https://doi.org/10.1007/978-1-4471-0745-3_10

Abstract

This paper addresses the discovery of surprising patterns. Recently, several authors have addressed the task of discovering surprising prediction rules. However, we do not focus on prediction rules, but rather on a quite different kind of pattern, namely the occurrence of Simpson's paradox. Intuitively, the fact that this is a paradox suggests that it has a great potential to be a surprising pattern for the user. With this motivation, we make the detection of Simpson's paradox the central goal of a data mining algorithm explicitly designed to discover surprising patterns. We present computational results showing surprising occurrences of the paradox in some public-domain data sets. In addition, we propose a method for ranking the discovered instances of the paradox in decreasing order of estimated degree of surprisingness.

Item Type: Conference or workshop item (Paper)
DOI/Identification number: 10.1007/978-1-4471-0745-3_10
Additional information: Proceedings Paper
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 02 Sep 2009 15:44 UTC
Last Modified: 09 Mar 2023 11:31 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/21712 (The current URI for this page, for reference purposes)

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