Domain driven data mining

Cao, L; Zhang, C

Domain driven data mining

Cao, L

Zhang, C

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Publication Type:: Chapter
Citation:: Data Mining and Knowledge Discovery Technologies, 2008, pp. 196 - 223
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2008-12-01	en_US
dc.identifier.citation	Data Mining and Knowledge Discovery Technologies, 2008, pp. 196 - 223	en_US
dc.identifier.isbn	9781599049601	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12416
dc.description.abstract	Quantitative intelligence-based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications. For instance, the usual demonstration of specific algorithms cannot support business users to take actions to their advantage and needs. We think this is due to quantitative intelligence focused data-driven philosophy. It either views data mining as an autonomous datadriven, trial-and-error process, or only analyzes business issues in an isolated, case-by-case manner. Based on experience and lessons learned from real-world data mining and complex systems, this article proposes a practical data mining methodology referred to as domain-driven data mining. On top of quantitative intelligence and hidden knowledge in data, domain-driven data mining aims to meta-synthesize quantitative intelligence and qualitative intelligence in mining complex applications in which human is in the loop. It targets actionable knowledge discovery in constrained environment for satisfying user preference. Domain-driven methodology consists of key components including understanding constrained environment, business-technical questionnaire, representing and involving domain knowledge, human-mining cooperation and interaction, constructing next-generation mining infrastructure, in-depth pattern mining and postprocessing, business interestingness and actionability enhancement, and loop-closed human-cooperated iterative refinement. Domain-driven data mining complements the data-driven methodology, the metasynthesis of qualitative intelligence and quantitative intelligence has potential to discover knowledge from complex systems, and enhance knowledge actionability for practical use by industry and business. © 2008, IGI Global.	en_US
dc.relation.ispartof	Data Mining and Knowledge Discovery Technologies	en_US
dc.relation.isbasedon	10.4018/978-1-59904-960-1.ch009	en_US
dc.title	Domain driven data mining	en_US
dc.type	Chapter
utslib.for	080109 Pattern Recognition and Data Mining	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
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
pubs.organisational-group	/University of Technology Sydney/Strength - ACRI - Australia China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Quantitative intelligence-based traditional data mining is facing grand challenges from real-world enterprise and cross-organization applications. For instance, the usual demonstration of specific algorithms cannot support business users to take actions to their advantage and needs. We think this is due to quantitative intelligence focused data-driven philosophy. It either views data mining as an autonomous datadriven, trial-and-error process, or only analyzes business issues in an isolated, case-by-case manner. Based on experience and lessons learned from real-world data mining and complex systems, this article proposes a practical data mining methodology referred to as domain-driven data mining. On top of quantitative intelligence and hidden knowledge in data, domain-driven data mining aims to meta-synthesize quantitative intelligence and qualitative intelligence in mining complex applications in which human is in the loop. It targets actionable knowledge discovery in constrained environment for satisfying user preference. Domain-driven methodology consists of key components including understanding constrained environment, business-technical questionnaire, representing and involving domain knowledge, human-mining cooperation and interaction, constructing next-generation mining infrastructure, in-depth pattern mining and postprocessing, business interestingness and actionability enhancement, and loop-closed human-cooperated iterative refinement. Domain-driven data mining complements the data-driven methodology, the metasynthesis of qualitative intelligence and quantitative intelligence has potential to discover knowledge from complex systems, and enhance knowledge actionability for practical use by industry and business. © 2008, IGI Global.

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http://hdl.handle.net/10453/12416