BruteSuppression: a size reduction method for Apriori rule sets

Hills, Jon, Bagnall, Anthony, de la Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826 and Richards, Graeme (2013) BruteSuppression: a size reduction method for Apriori rule sets. Journal of Intelligent Information Systems, 40 (3). pp. 431-454. ISSN 0925-9902

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Abstract

Association rule mining can provide genuine insight into the data being analysed; however, rule sets can be extremely large, and therefore difficult and time-consuming for the user to interpret. We propose reducing the size of Apriori rule sets by removing overlapping rules, and compare this approach with two standard methods for reducing rule set size: increasing the minimum confidence parameter, and increasing the minimum antecedent support parameter. We evaluate the rule sets in terms of confidence and coverage, as well as two rule interestingness measures that favour rules with antecedent conditions that are poor individual predictors of the target class, as we assume that these represent potentially interesting rules. We also examine the distribution of the rules graphically, to assess whether particular classes of rules are eliminated. We show that removing overlapping rules substantially reduces rule set size in most cases, and alters the character of a rule set less than if the standard parameters are used to constrain the rule set to the same size. Based on our results, we aim to extend the Apriori algorithm to incorporate the suppression of overlapping rules.

Item Type: Article
Faculty \ School: Faculty of Science
Faculty of Science > School of Computing Sciences
Depositing User: Users 2731 not found.
Date Deposited: 25 Jan 2013 13:11
Last Modified: 31 Oct 2022 11:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/40976
DOI: 10.1007/s10844-012-0232-5

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