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

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dc.contributor.author Zhang, H
dc.contributor.author Zhao, Y
dc.contributor.author Cao, L
dc.contributor.author Zhang, C
dc.contributor.author Bohlscheid, H
dc.contributor.editor Koh, YS
dc.contributor.editor Rountree, N
dc.date.accessioned 2010-06-16T04:55:25Z
dc.date.issued 2010-01
dc.identifier.citation Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event, 2010, 1, pp. 66 - 75
dc.identifier.isbn 978-1-60566-754-6
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.
dc.publisher IGI Global
dc.title Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event
dc.type Chapter
dc.parent Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event
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 080610 Information Systems Organisation
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 011221
dc.personcode 034535
dc.personcode 998488
dc.personcode 995032
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
dc.description.keywords Australia
dc.description.keywords experiential learning
dc.description.keywords natural hazards
dc.description.keywords peri-urban landscapes
dc.description.keywords risk communication
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Strength - Quantum Computation and Intelligent Systems
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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