Domain-Driven Data Mining: A Practical Methodology

Cao, L; Zhang, C

Domain-Driven Data Mining: A Practical Methodology

Cao, L

Zhang, C

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Publication Type:: Journal Article
Citation:: International Journal of Data Warehousing and Mining (IJDWM), 2006, 2 (4), pp. 49 - 65
Issue Date:: 2006-01-01

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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	2006-01-01	en_US
dc.identifier.citation	International Journal of Data Warehousing and Mining (IJDWM), 2006, 2 (4), pp. 49 - 65	en_US
dc.identifier.issn	1548-3924	en_US
dc.identifier.uri	http://hdl.handle.net/10453/6137
dc.description.abstract	Extant data mining is based on data-driven methodologies. It either views data mining as an autonomous data-driven, trial-and-error process or only analyzes business issues in an isolated, case-by-case manner. As a result, very often the knowledge discovered generally is not interesting to real business needs. Therefore, this article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user preference. The domain-driven data mining consists of a DDID-PD framework that considers key components such as constraint-based context, integrating domain knowledge, human-machine cooperation, in-depth mining, actionability enhancement, and iterative refinement process. We also illustrate some examples in mining actionable correlations in Australian Stock Exchange, which show that domain-driven data mining has potential to improve further the actionability of patterns for practical use by industry and business. © 2006, IGI Global. All rights reserved.	en_US
dc.relation.ispartof	International Journal of Data Warehousing and Mining (IJDWM)	en_US
dc.relation.isbasedon	10.4018/jdwm.2006100103	en_US
dc.subject.classification	Information Systems	en_US
dc.title	Domain-Driven Data Mining: A Practical Methodology	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	2	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0804 Data Format	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.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	2	en_US

Abstract:

Extant data mining is based on data-driven methodologies. It either views data mining as an autonomous data-driven, trial-and-error process or only analyzes business issues in an isolated, case-by-case manner. As a result, very often the knowledge discovered generally is not interesting to real business needs. Therefore, this article proposes a practical data mining methodology referred to as domain-driven data mining, which targets actionable knowledge discovery in a constrained environment for satisfying user preference. The domain-driven data mining consists of a DDID-PD framework that considers key components such as constraint-based context, integrating domain knowledge, human-machine cooperation, in-depth mining, actionability enhancement, and iterative refinement process. We also illustrate some examples in mining actionable correlations in Australian Stock Exchange, which show that domain-driven data mining has potential to improve further the actionability of patterns for practical use by industry and business. © 2006, IGI Global. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/6137