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Sunderland Repository records the research produced by the University of Sunderland including practice-based research and theses.

Integrating Bayesian networks and Simpson's paradox in data mining

Freitas, Alex, McGarry, Kenneth and Correa, Elon (2007) Integrating Bayesian networks and Simpson's paradox in data mining. Causality and Probability in the Sciences. . College Publications, United Kingdom, University of Kent. ISBN 1-904987-35-4

Item Type: Book


This paper proposes to integrate two very different kinds of methods for data mining, namely the construction of Bayesian networks from data and the detection of occurrences of Simpson’s paradox. The former aims at discovering potentially causal knowledge in the data, whilst the latter aims at detecting surprising patterns in the data. By integrating these two kinds of methods we can hope fully discover patterns which are more likely to be useful to the user, a challenging data mining goal which is under-explored in the literature. The proposed integration method involves two approaches. The first approach uses the detection of occurrences of Simpson’s paradox
as a preprocessing for a more effective construction of Bayesian networks; whilst the second approach uses the construction of a Bayesian network from data as a preprocessing for the detection of occurrences of Simpson’s paradox

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Depositing User: Kenneth McGarry


Item ID: 15453
ISBN: 1-904987-35-4
Official URL:

Users with ORCIDS

ORCID for Kenneth McGarry: ORCID iD

Catalogue record

Date Deposited: 20 Feb 2023 09:50
Last Modified: 20 Mar 2023 11:15


Author: Kenneth McGarry ORCID iD
Author: Alex Freitas
Author: Elon Correa
Author: [error in script] [error in script]

University Divisions

Faculty of Technology > School of Computer Science


Computing > Artificial Intelligence

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