Domain-driven in-depth pattern discovery: A practical methodology

Cao, L; Schurmann, R; Zhang, C

Domain-driven in-depth pattern discovery: A practical methodology

Cao, L

Schurmann, R Zhang, C

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Publication Type:: Conference Proceeding
Citation:: AusDM 2005 Proc. - 4th Australasian Data Mining Conf. - Collocated with the 18th Australian Joint Conf. on Artificial Intelligence, AI 2005 and the 2nd Australian Conf. on Artifical Life, ACAL 2005, 2005, pp. 101 - 114
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.author	Schurmann, R	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	AusDM 2005 Proc. - 4th Australasian Data Mining Conf. - Collocated with the 18th Australian Joint Conf. on Artificial Intelligence, AI 2005 and the 2nd Australian Conf. on Artifical Life, ACAL 2005, 2005, pp. 101 - 114	en_US
dc.identifier.isbn	1863657169	en_US
dc.identifier.isbn	9781863657167	en_US
dc.identifier.uri	http://hdl.handle.net/10453/1903
dc.description.abstract	Traditional data mining is a data-driven trial-and-error process. The patterns discovered via predefined models in the above process are generic patterns. Generally, they are often not really interesting to constraint-based real business, hi order to work out patterns that are of interest and actionable to the real world, in-depth patterns are often essential. This type of pattern discovery is more likely to be a business or industry domain-driven human-machine-cooperated process. The use of in-depth patterns requires the development of a more practical methodology, than is presently available for guiding real-world data mining. This paper proposes such a practical data mining methodology, referred to as domain-driven in-depth pattern discovery (DDID-PD). The main idea of the DDID-PD methodology is to mine in-depth patterns through domain-driven iterative human-machine interaction in a constraint-based context. Using this methodology as a basis, we demonstrate some of our work in mining in-depth correlations in Australian Stock Exchange (ASX) data and preliminary research on developing a quality knowledge base for Centrelink interventions. The deployment of DDID-PD to ASX data mining tasks has shown that the methodology is practical and has potential for further improving the analysis of large quantities of data to identify patterns for practical use by industry and business. © 2013.	en_US
dc.relation.ispartof	AusDM 2005 Proc. - 4th Australasian Data Mining Conf. - Collocated with the 18th Australian Joint Conf. on Artificial Intelligence, AI 2005 and the 2nd Australian Conf. on Artifical Life, ACAL 2005	en_US
dc.title	Domain-driven in-depth pattern discovery: A practical methodology	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, Australia	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	open_access
pubs.publication-status	Published	en_US

Abstract:

Traditional data mining is a data-driven trial-and-error process. The patterns discovered via predefined models in the above process are generic patterns. Generally, they are often not really interesting to constraint-based real business, hi order to work out patterns that are of interest and actionable to the real world, in-depth patterns are often essential. This type of pattern discovery is more likely to be a business or industry domain-driven human-machine-cooperated process. The use of in-depth patterns requires the development of a more practical methodology, than is presently available for guiding real-world data mining. This paper proposes such a practical data mining methodology, referred to as domain-driven in-depth pattern discovery (DDID-PD). The main idea of the DDID-PD methodology is to mine in-depth patterns through domain-driven iterative human-machine interaction in a constraint-based context. Using this methodology as a basis, we demonstrate some of our work in mining in-depth correlations in Australian Stock Exchange (ASX) data and preliminary research on developing a quality knowledge base for Centrelink interventions. The deployment of DDID-PD to ASX data mining tasks has shown that the methodology is practical and has potential for further improving the analysis of large quantities of data to identify patterns for practical use by industry and business. © 2013.

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http://hdl.handle.net/10453/1903