Actionable Knowledge Discovery

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dc.contributor.author Cao, L
dc.contributor.editor Mehdi Khosrow-Pour
dc.date.accessioned 2010-05-28T09:38:08Z
dc.date.issued 2009-01
dc.identifier.citation Encyclopedia of Information Science and Technology, 2009, 1, pp. 8 - 13
dc.identifier.isbn 978-1-60566-026-4
dc.identifier.other B1 en_US
dc.identifier.uri http://hdl.handle.net/10453/7840
dc.description.abstract Actionable knowledge discovery is selected as one of the greatest challenges (Ankerst, 2002; Fayyad, Shapiro, & Uthurusamy, 2003) of next-generation knowledge discovery in database (KDD) studies (Han & Kamber, 2006). In the existing data mining, often mined patterns are nonactionable to real user needs. To enhance knowledge actionability, domain-related social intelligence is substantially essential (Cao et al., 2006b). The involvement of domain-related social intelligence into data mining leads to domaindriven data mining (Cao & Zhang, 2006a, 2007a), which complements traditional data-centered mining methodology. Domain-related social intelligence consists of intelligence of human, domain, environment, society and cyberspace, which complements data intelligence. The extension of KDD toward domain-driven data mining involves many challenging but promising research and development issues in KDD. Studies in regard to these issues may promote the paradigm shift of KDD from data-centered interesting pattern mining to domain-driven actionable knowledge discovery, and the deployment shift from simulated data set-based to real-life data and business environment-oriented as widely predicted.
dc.publisher IGI Global
dc.relation.isbasedon 10.4018/978-1-60566-026-4.ch002
dc.title Actionable Knowledge Discovery
dc.type Chapter
dc.parent Encyclopedia of Information Science and Technology
dc.journal.number en_US
dc.publocation Hershey, PA, USA en_US
dc.publocation Hershey, PA, USA
dc.publocation Hershey, PA, USA
dc.publocation Hershey, PA, USA
dc.publocation Hershey, PA, USA
dc.identifier.startpage 8 en_US
dc.identifier.endpage 13 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080101 Adaptive Agents and Intelligent Robotics
dc.for 080110 Simulation and Modelling
dc.for 080109 Pattern Recognition and Data Mining
dc.personcode 034535
dc.percentage 55 en_US
dc.classification.name Pattern Recognition and Data Mining en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.edition 1 en_US
dc.edition 1
dc.edition 1
dc.edition 1
dc.edition 1
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords en_US
dc.description.keywords NA en_US
dc.description.keywords NA
dc.description.keywords NA
dc.description.keywords NA
dc.description.keywords NA
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


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