Domain Driven Data Mining (D3M)

Cao, L

Domain Driven Data Mining (D3M)

Cao, L

Permalink

Publisher:: IEEE Computer Society
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2008 IEEE International Conference on Data Mining Workshops, 2008, pp. 74 - 76
Issue Date:: 2008-01

Closed Access

	Filename	Description	Size
	2008001497.pdf		134.39 kB	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	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.contributor.editor	Bonchi, F	en_US
dc.contributor.editor	Berendt, B	en_US
dc.contributor.editor	Giannotti, F	en_US
dc.contributor.editor	Gunopulos, D	en_US
dc.contributor.editor	Turini, F	en_US
dc.contributor.editor	Zaniolo, C	en_US
dc.contributor.editor	Ramakrishnan, N	en_US
dc.contributor.editor	Wu, X	en_US
dc.date	2008-12-15	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Proceedings of the 2008 IEEE International Conference on Data Mining Workshops, 2008, pp. 74 - 76	en_US
dc.identifier.isbn	978-0-7695-3503-6	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10818
dc.description.abstract	In deploying data mining into the real-world business, we have to cater for business scenarios, organizational factors, user preferences and business needs. However, the current data mining algorithms and tools often stop at the delivery of patterns satisfying expected technical interestingness. Business people are not informed about how and what to do to take over the technical deliverables. The gap between academia and business has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. To narrow down the gap, cater for realworld factors relevant to data mining, and make data mining workable in supporting decision-making actions in the real world, we propose the methodology of Domain Driven Data Mining (D3M for short). D3M aims to construct next-generation methodologies, techniques and tools for a possible paradigm shift from data-centered hidden pattern mining to domain-driven actionable knowledge delivery. In this talk, we address the concept map of D3M, theoretical underpinnings, several general and flexible frameworks, research issues, possible directions, application areas etc. related to D3M. Real-world case studies in financial data mining and social security mining are demonstrated to show the effectiveness and applicability of D3M in both research and development of real-world challenging problems.	en_US
dc.publisher	IEEE Computer Society	en_US
dc.relation.ispartof	Proceedings of the 2008 IEEE International Conference on Data Mining Workshops	en_US
dc.relation.ispartof	IEEE International Conference on Data Mining	en_US
dc.relation.isbasedon	10.1109/ICDMW.2008.4	en_US
dc.rights	© 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	Domain Driven Data Mining (D3M)	en_US
dc.type	Conference Proceeding
utslib.location	Washington, DC, USA	en_US
utslib.location.activity	Pisa, Italy	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Pisa, Italy	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
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
utslib.copyright.status	closed_access
pubs.consider-herdc	true	en_US
pubs.place-of-publication	Washington, DC, USA	en_US
pubs.start-date	2008-12-15	en_US

Abstract:

In deploying data mining into the real-world business, we have to cater for business scenarios, organizational factors, user preferences and business needs. However, the current data mining algorithms and tools often stop at the delivery of patterns satisfying expected technical interestingness. Business people are not informed about how and what to do to take over the technical deliverables. The gap between academia and business has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. To narrow down the gap, cater for realworld factors relevant to data mining, and make data mining workable in supporting decision-making actions in the real world, we propose the methodology of Domain Driven Data Mining (D3M for short). D3M aims to construct next-generation methodologies, techniques and tools for a possible paradigm shift from data-centered hidden pattern mining to domain-driven actionable knowledge delivery. In this talk, we address the concept map of D3M, theoretical underpinnings, several general and flexible frameworks, research issues, possible directions, application areas etc. related to D3M. Real-world case studies in financial data mining and social security mining are demonstrated to show the effectiveness and applicability of D3M in both research and development of real-world challenging problems.

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

http://hdl.handle.net/10453/10818