Recent advances of exception mining in stock market

Luo, C; Zhao, Y; Luo, D; Ou, Y; Liu, L

Recent advances of exception mining in stock market

Luo, C Zhao, Y Luo, D Ou, Y Liu, L

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Publication Type:: Chapter
Citation:: Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions, 2009, pp. 212 - 232
Issue Date:: 2009-12-01

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Field	Value	Language
dc.contributor.author	Luo, C	en_US
dc.contributor.author	Zhao, Y	en_US
dc.contributor.author	Luo, D	en_US
dc.contributor.author	Ou, Y	en_US
dc.contributor.author	Liu, L	en_US
dc.date.issued	2009-12-01	en_US
dc.identifier.citation	Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions, 2009, pp. 212 - 232	en_US
dc.identifier.isbn	9781605668161	en_US
dc.identifier.uri	http://hdl.handle.net/10453/14284
dc.description.abstract	This chapter aims to provide a comprehensive survey of the current advanced technologies of exception mining in stock market. The stock market surveillance is to identify market anomalies so as to provide a fair and efficient trading platform. The technologies of market surveillance developed from simple statistical rules to more advanced technologies, such as data mining and artificial intelligent. This chapter provides the basic concepts of exception mining in stock market. Then the recent advances of exception mining in this domain are presented and the key issues are discussed. The advantages and disadvantages of the advanced technologies are analyzed. Furthermore, our model of OMM (Outlier Mining on Multiple time series) is introduced. Finally, this chapter points out the future research directions and related issues in reality. © 2010, IGI Global.	en_US
dc.relation.ispartof	Evolving Application Domains of Data Warehousing and Mining: Trends and Solutions	en_US
dc.relation.isbasedon	10.4018/978-1-60566-816-1.ch010	en_US
dc.title	Recent advances of exception mining in stock market	en_US
dc.type	Chapter
utslib.for	0801 Artificial Intelligence and Image Processing	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
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

This chapter aims to provide a comprehensive survey of the current advanced technologies of exception mining in stock market. The stock market surveillance is to identify market anomalies so as to provide a fair and efficient trading platform. The technologies of market surveillance developed from simple statistical rules to more advanced technologies, such as data mining and artificial intelligent. This chapter provides the basic concepts of exception mining in stock market. Then the recent advances of exception mining in this domain are presented and the key issues are discussed. The advantages and disadvantages of the advanced technologies are analyzed. Furthermore, our model of OMM (Outlier Mining on Multiple time series) is introduced. Finally, this chapter points out the future research directions and related issues in reality. © 2010, IGI Global.

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

http://hdl.handle.net/10453/14284