Automated and Perceptual Data Mining of Stock Market Data

Keith Nesbitt

Automated and Perceptual Data Mining of Stock Market Data

Keith Nesbitt

Research output: Book chapter/Published conference paper › Conference paper

Abstract

Data mining large abstract data sets for useful patterns is an attractive proposition. It may, for example, reveal useful trading rules in stock market data. There are two basic types of data mining, Automated data mining tools use algorithms that allow the computer to search the data. Perceptual data mining tools present the data to the user's senses (vision, hearing, touch) in a way that the user can search for useful patterns. The two methods are not disjoint, as rules discovered with the user's perception can then be automated. This paper describes a case study, where a visual-auditory interface was used to uncover patterns in stock market data. Results from a formal evaluation are reported. The paper also includes a discussion on how to incorporate these results into an automated tool using an agent framework.

Original language	English
Title of host publication	ANZIIS 2003, Proceedings of the Eight Australian and New Zealand Intelligent Information Systems Conference
Editors	Brian Lovell, Duncan Campbell, Clinton Fookes, Anthony Maeder
Place of Publication	Brisbane, Australia
Publisher	Queensland University of Technology
Pages	145-150
Number of pages	6
ISBN (Electronic)	1741970392
Publication status	Published - 2003
Event	Australian and New Zealand Intelligent Information Systems Conference - Sydney, Australia, Australia Duration: 10 Dec 2003 → 12 Dec 2003

Conference

Conference	Australian and New Zealand Intelligent Information Systems Conference
Country	Australia
Period	10/12/03 → 12/12/03

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Cite this

Nesbitt, K. (2003). Automated and Perceptual Data Mining of Stock Market Data. In B. Lovell, D. Campbell, C. Fookes, & A. Maeder (Eds.), ANZIIS 2003, Proceedings of the Eight Australian and New Zealand Intelligent Information Systems Conference (pp. 145-150). Queensland University of Technology.

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title = "Automated and Perceptual Data Mining of Stock Market Data",

abstract = "Data mining large abstract data sets for useful patterns is an attractive proposition. It may, for example, reveal useful trading rules in stock market data. There are two basic types of data mining, Automated data mining tools use algorithms that allow the computer to search the data. Perceptual data mining tools present the data to the user's senses (vision, hearing, touch) in a way that the user can search for useful patterns. The two methods are not disjoint, as rules discovered with the user's perception can then be automated. This paper describes a case study, where a visual-auditory interface was used to uncover patterns in stock market data. Results from a formal evaluation are reported. The paper also includes a discussion on how to incorporate these results into an automated tool using an agent framework.",

author = "Keith Nesbitt",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = Brisbane, Australia: Queensland University of Technology, 2003. editor/s (773b) = Lovell, Brian; Campbell, Duncan; Fookes, Clinton; Maeder, Anthony; Event dates (773o) = 10/12/2003 - 12/12/2003; Parent title (773t) = Australian and New Zealand Intelligent Information Systems Conference.; Australian and New Zealand Intelligent Information Systems Conference ; Conference date: 10-12-2003 Through 12-12-2003",

year = "2003",

language = "English",

pages = "145--150",

editor = "Brian Lovell and Duncan Campbell and Clinton Fookes and Anthony Maeder",

booktitle = "ANZIIS 2003, Proceedings of the Eight Australian and New Zealand Intelligent Information Systems Conference",

publisher = "Queensland University of Technology",

address = "Australia",

}

Nesbitt, K 2003, Automated and Perceptual Data Mining of Stock Market Data. in B Lovell, D Campbell, C Fookes & A Maeder (eds), ANZIIS 2003, Proceedings of the Eight Australian and New Zealand Intelligent Information Systems Conference. Queensland University of Technology, Brisbane, Australia, pp. 145-150, Australian and New Zealand Intelligent Information Systems Conference, Australia, 10/12/03.

Automated and Perceptual Data Mining of Stock Market Data. / Nesbitt, Keith.

ANZIIS 2003, Proceedings of the Eight Australian and New Zealand Intelligent Information Systems Conference. ed. / Brian Lovell; Duncan Campbell; Clinton Fookes; Anthony Maeder. Brisbane, Australia : Queensland University of Technology, 2003. p. 145-150.

Research output: Book chapter/Published conference paper › Conference paper

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N2 - Data mining large abstract data sets for useful patterns is an attractive proposition. It may, for example, reveal useful trading rules in stock market data. There are two basic types of data mining, Automated data mining tools use algorithms that allow the computer to search the data. Perceptual data mining tools present the data to the user's senses (vision, hearing, touch) in a way that the user can search for useful patterns. The two methods are not disjoint, as rules discovered with the user's perception can then be automated. This paper describes a case study, where a visual-auditory interface was used to uncover patterns in stock market data. Results from a formal evaluation are reported. The paper also includes a discussion on how to incorporate these results into an automated tool using an agent framework.

AB - Data mining large abstract data sets for useful patterns is an attractive proposition. It may, for example, reveal useful trading rules in stock market data. There are two basic types of data mining, Automated data mining tools use algorithms that allow the computer to search the data. Perceptual data mining tools present the data to the user's senses (vision, hearing, touch) in a way that the user can search for useful patterns. The two methods are not disjoint, as rules discovered with the user's perception can then be automated. This paper describes a case study, where a visual-auditory interface was used to uncover patterns in stock market data. Results from a formal evaluation are reported. The paper also includes a discussion on how to incorporate these results into an automated tool using an agent framework.

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