Automated and Perceptual Data Mining of Stock Market Data

Keith Nesbitt

    Research output: Book chapter/Published conference paperConference 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 languageEnglish
    Title of host publicationANZIIS 2003, Proceedings of the Eight Australian and New Zealand Intelligent Information Systems Conference
    EditorsBrian Lovell, Duncan Campbell, Clinton Fookes, Anthony Maeder
    Place of PublicationBrisbane, Australia
    PublisherQueensland University of Technology
    Pages145-150
    Number of pages6
    ISBN (Electronic)1741970392
    Publication statusPublished - 2003
    EventAustralian and New Zealand Intelligent Information Systems Conference - Sydney, Australia, Australia
    Duration: 10 Dec 200312 Dec 2003

    Conference

    ConferenceAustralian and New Zealand Intelligent Information Systems Conference
    CountryAustralia
    Period10/12/0312/12/03

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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.