A human-friendly MAS for mining stock data

Ni, J; Zhang, C

A human-friendly MAS for mining stock data

Ni, J Zhang, C

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Publication Type:: Conference Proceeding
Citation:: Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops Proceedings), 2006, pp. 19 - 22
Issue Date:: 2006-01-01

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Field	Value	Language
dc.contributor.author	Ni, J	en_US
dc.contributor.author	Zhang, C https://orcid.org/0000-0001-5715-7154	en_US
dc.date.issued	2006-01-01	en_US
dc.identifier.citation	Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops Proceedings), 2006, pp. 19 - 22	en_US
dc.identifier.isbn	0769527493	en_US
dc.identifier.isbn	9780769527499	en_US
dc.identifier.uri	http://hdl.handle.net/10453/2763
dc.description.abstract	Mining stock data can be beneficial to the participants and researchers in the stock market. However, it is very difficult for a normal trader or researcher to apply data mining techniques to the data on his own due to the complexity involved in the whole data mining process. In this paper, we present a multi-agent system that can help users easily deal with their data mining jobs on stock data. This system guides users to specify their mining tasks by simply specifying the data sets to be mined and selecting pre-defined and/or user-added data mining agents. This approach offers normal traders a practical and flexible solution to mining stock data. © 2006 IEEE.	en_US
dc.relation.ispartof	Proceedings - 2006 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT 2006 Workshops Proceedings)	en_US
dc.relation.isbasedon	10.1109/WI-IATW.2006.12	en_US
dc.title	A human-friendly MAS for mining stock data	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Hong Kong, China	en_US
dc.location.activity	Brisbane, Australia
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 - 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:

Mining stock data can be beneficial to the participants and researchers in the stock market. However, it is very difficult for a normal trader or researcher to apply data mining techniques to the data on his own due to the complexity involved in the whole data mining process. In this paper, we present a multi-agent system that can help users easily deal with their data mining jobs on stock data. This system guides users to specify their mining tasks by simply specifying the data sets to be mined and selecting pre-defined and/or user-added data mining agents. This approach offers normal traders a practical and flexible solution to mining stock data. © 2006 IEEE.

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http://hdl.handle.net/10453/2763