Knowledge actionability: satisfying technical and business interestingness

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Show simple item record Cao, L Luo, D Zhang, C 2009-12-21T02:31:17Z 2007-01
dc.identifier.citation International Journal of Business Intelligence and Data Mining, 2007, 2 (4), pp. 496 - 514
dc.identifier.issn 1743-8187
dc.identifier.other C1 en_US
dc.description.abstract Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs.
dc.publisher Inderscience Publishers
dc.relation.isbasedon 10.1504/IJBIDM.2007.016385
dc.title Knowledge actionability: satisfying technical and business interestingness
dc.type Journal Article
dc.parent International Journal of Business Intelligence and Data Mining
dc.journal.volume 4
dc.journal.volume 2
dc.journal.number 4 en_US
dc.publocation Bucks, UK en_US
dc.identifier.startpage 496 en_US
dc.identifier.endpage 514 en_US QCIS Investment Core en_US
dc.conference Verified OK en_US
dc.for 0804 Data Format
dc.personcode 011221
dc.personcode 034535
dc.personcode 996795
dc.percentage 100 en_US Data Format en_US
dc.classification.type FOR-08 en_US
dc.description.keywords data mining; actionable knowledge; technical interestingness; business decision making; domain driven data mining; profit mining. en_US
dc.description.keywords NA
dc.description.keywords data mining
dc.description.keywords actionable knowledge
dc.description.keywords technical interestingness
dc.description.keywords business decision making
dc.description.keywords domain driven data mining
dc.description.keywords profit mining.
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 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)

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