Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event

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dc.contributor.author Zhang, Huaifeng en_US
dc.contributor.author Zhao, Yanchang en_US
dc.contributor.author Cao, Longbing en_US
dc.contributor.author Zhang, Chengqi en_US
dc.contributor.author Bohlscheid, Hans-Michael en_US
dc.contributor.editor Yun Sing Koh; Nathan Rountree en_US
dc.date.accessioned 2010-06-16T04:55:25Z
dc.date.available 2010-06-16T04:55:25Z
dc.date.issued 2010 en_US
dc.identifier 2009001894 en_US
dc.identifier.citation Zhang Huaifeng et al. 2010, 'Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event', in NA (ed.), IGI Global, Hershey, Pennsylvania, pp. 66-75. en_US
dc.identifier.issn 978-1-60566-754-6 en_US
dc.identifier.other B1 en_US
dc.identifier.uri http://hdl.handle.net/10453/11635
dc.description.abstract In this chapter, the authors propose a novel framework for rare class association rule mining. In each class association rule, the right-hand is a target class while the left-hand may contain one or more attributes. This algorithm is focused on the multiple imbalanced attributes on the left-hand. In the proposed framework, the rules with and without imbalanced attributes are processed in parallel. The rules without imbalanced attributes are mined through a standard algorithm while the rules with imbalanced attributes are mined based on newly defined measurements. Through simple transformation, these measurements can be in a uniform space so that only a few parameters need to be specified by user. In the case study, the proposed algorithm is applied in the social security field. Although some attributes are severely imbalanced, rules with a minority of imbalanced attributes have been mined efficiently. en_US
dc.language English en_US
dc.publisher IGI Global en_US
dc.relation.isbasedon NA en_US
dc.title Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event en_US
dc.parent Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Hershey, Pennsylvania en_US
dc.identifier.startpage 66 en_US
dc.identifier.endpage 75 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080109 en_US
dc.personcode 995032 en_US
dc.personcode 998488 en_US
dc.personcode 034535 en_US
dc.personcode 011221 en_US
dc.personcode 00102347 en_US
dc.percentage 70 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
dc.edition 1 en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords NA en_US


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