Actionable Knowledge Discovery

Cao, L

Actionable Knowledge Discovery

Cao, L

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Publisher:: IGI Global
Publication Type:: Chapter
Citation:: Encyclopedia of Information Science and Technology, 2009, 1, pp. 8 - 13
Issue Date:: 2009-01

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Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.editor	Mehdi Khosrow-Pour	en_US
dc.date.issued	2009-01	en_US
dc.identifier.citation	Encyclopedia of Information Science and Technology, 2009, 1, pp. 8 - 13	en_US
dc.identifier.isbn	978-1-60566-026-4	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.	en_US
dc.publisher	IGI Global	en_US
dc.relation.ispartof	Encyclopedia of Information Science and Technology	en_US
dc.relation.isbasedon	10.4018/978-1-60566-026-4.ch002	en_US
dc.title	Actionable Knowledge Discovery	en_US
dc.type	Chapter
utslib.location	Hershey, PA, USA	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.citation.edition	1	en_US
pubs.embargo.period	Not known	en_US
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 - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.edition	1	en_US
pubs.place-of-publication	Hershey, PA, USA	en_US

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.

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http://hdl.handle.net/10453/7840