A novel hybrid approach for interestingness analysis of classification rules

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dc.citation.epage496en_US
dc.citation.spage485en_US
dc.citation.volumeNumber3609en_US
dc.contributor.authorAydın, Tolgaen_US
dc.contributor.authorGüvenir, Halil Altayen_US
dc.coverage.spatialNiigata, Japan, June 23-27, 2003en_US
dc.coverage.spatialKanazawa, Japan, May 31 - June 4, 2004en_US
dc.date.accessioned2016-02-08T11:43:41Z
dc.date.available2016-02-08T11:43:41Z
dc.date.issued2007en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionConference name: JSAI 2003 and JSAI 2004 Conferences and Workshopsen_US
dc.descriptionDate of Conference: June 23-27 2003, May 31 - June 4 2004en_US
dc.description.abstractData mining is the efficient discovery of patterns in large databases, and classification rules are perhaps the most important type of patterns in data mining applications. However, the number of such classification rules is generally very big that selection of interesting ones among all discovered rules becomes an important task. In this paper, factors related to the interestingness of a rule are investigated and some new factors are proposed. Following this, an interactive rule interestingness-learning algorithm (IRIL) is developed to automatically label the classification rules either as "interesting" or "uninteresting" with limited user participation. In our study, VFP (Voting Feature Projections), a feature projection based incremental classification learning algorithm, is also developed in the framework of IRIL. The concept description learned by the VFP algorithm constitutes a novel hybrid approach for interestingness analysis of classification rules. © Springer-Verlag Berlin Heidelberg 2007.en_US
dc.identifier.doi10.1007/978-3-540-71009-7_46en_US
dc.identifier.doi10.1007/978-3-540-71009-7_46en_US
dc.identifier.doi10.1007/978-3-540-71009-7en_US
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11693/27077
dc.language.isoEnglishen_US
dc.publisherSpringer, Berlin, Heidelbergen_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-540-71009-7_46en_US
dc.relation.isversionofhttps://doi.org/10.1007/978-3-540-71009-7en_US
dc.source.titleNew Frontiers in Artificial Intelligenceen_US
dc.subjectHybrid approachen_US
dc.subjectInterestingness factoren_US
dc.subjectClassification ruleen_US
dc.subjectNominal featureen_US
dc.subjectAction abilityen_US
dc.titleA novel hybrid approach for interestingness analysis of classification rulesen_US
dc.typeConference Paperen_US
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