Using classification to evaluate the output of confidence-based association rule mining

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dc.contributor.authorMutter, Stefan
dc.contributor.authorHall, Mark A.
dc.contributor.authorFrank, Eibe
dc.coverage.spatialConference held at Cairns, Australiaen_NZ
dc.date.accessioned2008-11-21T03:42:53Z
dc.date.available2008-11-21T03:42:53Z
dc.date.issued2005
dc.description.abstractAssociation rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for interesting rules. This fact and the large number of rules make it difficult to compare the output of confidence-based association rule miners. This paper explores the use of classification performance as a metric for evaluating their output. Previous work on forming classifiers from association rules has focussed on accurate classification, whereas we concentrate on using the properties of the resulting classifiers as a basis for comparing confidence-based association rule learners. Therefore, we present experimental results on 12 UCI datasets showing that the quality of small rule sets generated by Apriori can be improved by using the predictive Apriori algorithm. We also show that CBA, the standard method for classification using association rules, is generally inferior to standard rule learners concerning both running time and size of rule sets.en_US
dc.identifier.citationMutter, S., Hall, M.A. & Frank, E. (2005). Using classification to evaluate the output of confidence-based association rule mining. In G.I. Webb & Xinghuo Yu(Eds.), Proceedings of 17th Australian Joint Conference on Artificial Intelligence, Cairns, Australia, December 4-6, 2004.(pp. 538-549). Berlin: Springer.en_US
dc.identifier.doi10.1007/978-3-540-30549-1_47en_US
dc.identifier.urihttps://hdl.handle.net/10289/1449
dc.language.isoen
dc.publisherSpringeren_US
dc.relation.isPartOfAdvances in Artificial Intelligence: 17th Australian Joint Conference on Artificial Intelligenceen_NZ
dc.relation.urihttp://www.springerlink.com/content/2kapt2d8ymkcy108/?p=580313c600784f9aa64be453a1496b8b&pi=47en_US
dc.sourceAI 2004en_NZ
dc.subjectcomputer scienceen_US
dc.subjectrule miningen_US
dc.subjectMachine learning
dc.titleUsing classification to evaluate the output of confidence-based association rule miningen_US
dc.typeConference Contributionen_US
pubs.begin-page538en_NZ
pubs.elements-id15229
pubs.end-page549en_NZ
pubs.finish-date2004-12-06en_NZ
pubs.place-of-publicationHeidelbergen_NZ
pubs.start-date2004-12-04en_NZ
pubs.volumeLNAI 3339en_NZ
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