The evolution of KDD: Towards domain-driven data mining

Cao, L; Zhang, C

The evolution of KDD: Towards domain-driven data mining

Cao, L

Zhang, C

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Publication Type:: Journal Article
Citation:: International Journal of Pattern Recognition and Artificial Intelligence, 2007, 21 (4), pp. 677 - 692
Issue Date:: 2007-06-01

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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	2007-06-01	en_US
dc.identifier.citation	International Journal of Pattern Recognition and Artificial Intelligence, 2007, 21 (4), pp. 677 - 692	en_US
dc.identifier.issn	0218-0014	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4089
dc.description.abstract	Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information, in which domain intelligence may not be necessary in targeting the demonstration of an algorithm. Often knowledge discovered is not generally interesting to business needs. Comparably, realworld applications rely on knowledge for taking effective actions. In retrospect of the evolution of KDD, this paper briefly introduces domain-driven data mining to complement traditional KDD. Domain intelligenceis highlighted towards actionable knowledge discovery, which involves aspects such as domain knowledge, people, environment and evaluation. We illustrate it through mining activity patterns in social security data. © World Scientific Publishing Company.	en_US
dc.relation.ispartof	International Journal of Pattern Recognition and Artificial Intelligence	en_US
dc.relation.isbasedon	10.1142/S0218001407005612	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	The evolution of KDD: Towards domain-driven data mining	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	21	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1702 Cognitive Sciences	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	21	en_US

Abstract:

Traditionally, data mining is an autonomous data-driven trial-and-error process. Its typical task is to let data tell a story disclosing hidden information, in which domain intelligence may not be necessary in targeting the demonstration of an algorithm. Often knowledge discovered is not generally interesting to business needs. Comparably, realworld applications rely on knowledge for taking effective actions. In retrospect of the evolution of KDD, this paper briefly introduces domain-driven data mining to complement traditional KDD. Domain intelligenceis highlighted towards actionable knowledge discovery, which involves aspects such as domain knowledge, people, environment and evaluation. We illustrate it through mining activity patterns in social security data. © World Scientific Publishing Company.

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

http://hdl.handle.net/10453/4089