Introduction to domain driven data mining

Longbing, C

Introduction to domain driven data mining

Longbing, C

Permalink

Publication Type:: Chapter
Citation:: 2009, pp. 3 - 10
Issue Date:: 2009-12-01

Closed Access

	Filename	Description	Size
	2008001759OK.pdf		1.96 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Closed Access

This item is closed access and not available.

Full metadata record

Field	Value	Language
dc.contributor.author	Longbing, C https://orcid.org/0000-0003-1562-9429	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	2009, pp. 3 - 10	en_US
dc.identifier.isbn	9780387794198	en_US
dc.identifier.uri	http://hdl.handle.net/10453/12406
dc.description.abstract	The mainstream data mining faces critical challenges and lacks of soft power in solving real-world complex problems when deployed. Following the paradigm shift from 'data mining' to 'knowledge discovery', we believe much more thorough efforts are essential for promoting the wide acceptance and employment of knowledge discovery in real-world smart decision making. To this end, we expect a new paradigm shift from 'data-centered knowledge discovery' to 'domain-driven actionable knowledge discovery'. In the domain-driven actionable knowledge discovery, ubiquitous intelligence must be involved and meta-synthesized into the mining process, and an actionable knowledge discovery-based problem-solving system is formed as the space for data mining. This is the motivation and aim of developing Domain Driven Data Mining (D 3 M for short). This chapter briefs the main reasons, ideas and open issues in D 3 M. © 2009 Springer US.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0667060
dc.relation	http://purl.org/au-research/grants/arc/DP0773412
dc.relation	http://purl.org/au-research/grants/arc/LP0775041R1
dc.relation.isbasedon	10.1007/978-0-387-79420-4_1	en_US
dc.title	Introduction to domain driven data mining	en_US
dc.type	Chapter
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080611 Information Systems Theory	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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
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:

The mainstream data mining faces critical challenges and lacks of soft power in solving real-world complex problems when deployed. Following the paradigm shift from 'data mining' to 'knowledge discovery', we believe much more thorough efforts are essential for promoting the wide acceptance and employment of knowledge discovery in real-world smart decision making. To this end, we expect a new paradigm shift from 'data-centered knowledge discovery' to 'domain-driven actionable knowledge discovery'. In the domain-driven actionable knowledge discovery, ubiquitous intelligence must be involved and meta-synthesized into the mining process, and an actionable knowledge discovery-based problem-solving system is formed as the space for data mining. This is the motivation and aim of developing Domain Driven Data Mining (D 3 M for short). This chapter briefs the main reasons, ideas and open issues in D 3 M. © 2009 Springer US.

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

http://hdl.handle.net/10453/12406