F-trade 3.0: An agent-based integrated framework for Data mining experiments

Moemeng, P; Cao, L; Zhang, C

F-trade 3.0: An agent-based integrated framework for Data mining experiments

Moemeng, P Cao, L

Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008, 2008, pp. 612 - 615
Issue Date:: 2008-12-01

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Field	Value	Language
dc.contributor.author	Moemeng, P	en_US
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	2008-12-01	en_US
dc.identifier.citation	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008, 2008, pp. 612 - 615	en_US
dc.identifier.isbn	9780769534961	en_US
dc.identifier.uri	http://hdl.handle.net/10453/10734
dc.description.abstract	Data mining researches focus on algorithms that mine valuable patterns from particular domain. Apart from the theoretical research, experiments take a vast amount of effort to build. In this paper, we propose an integrated framework that utilises a multi-agent system to support the researchers to rapidly develop experiments. Moreover, the proposed framework allows extension and integration for future researches in mutual aspects of agent and data mining. The paper describes the details of the framework and also presents a sample implementation. © 2008 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008	en_US
dc.relation.isbasedon	10.1109/WIIAT.2008.230	en_US
dc.title	F-trade 3.0: An agent-based integrated framework for Data mining experiments	en_US
dc.type	Conference Proceeding
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080110 Simulation and Modelling	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
dc.location.activity	University of Technology, 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:

Data mining researches focus on algorithms that mine valuable patterns from particular domain. Apart from the theoretical research, experiments take a vast amount of effort to build. In this paper, we propose an integrated framework that utilises a multi-agent system to support the researchers to rapidly develop experiments. Moreover, the proposed framework allows extension and integration for future researches in mutual aspects of agent and data mining. The paper describes the details of the framework and also presents a sample implementation. © 2008 IEEE.

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http://hdl.handle.net/10453/10734