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Attribute Transformations on Numerical Databases

Applications to Stock Market and Economic Data

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Book cover Knowledge Discovery and Data Mining. Current Issues and New Applications (PAKDD 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1805))

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Abstract

The effects of attribute transformations on numerical data mining are investigated. Theoretical examples from classical mathematics are used to illustrate its critical-ness. The simplest kind of attribution transformations, linear transformations, is applied to stock market and economic data. Some useful “predictive” rules are generated. Here “predictive” is used in the sense that the logical patterns involve time elements.

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Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, San Jose State University, San Jose, California, 95192

    Tsau Young Lin & Joseph Tremba

  2. Berkeley Initiative in Soft Computing, Department of Electrical Engineering and Computer Science, University of California, Berkeley, California, 94720

    Tsau Young Lin

Authors
  1. Tsau Young Lin
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  2. Joseph Tremba
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Editor information

Editors and Affiliations

  1. Graduate School of Systems Management, Universiy of Tsukuba, 3-29-1 Otsuka, Bunkyo-ku, Tokyo, 112-0012, Japan

    Takao Terano

  2. Department of Computer Science and Engineering, Arizona State University, P.O. Box 875 406, Tempe, AZ, 85287-5406

    Huan Liu

  3. Department of Computer Science, National Tsing Hua University, Hsinchu, 300, Taiwan ROC

    Arbee L. P. Chen

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© 2000 Springer-Verlag Berlin Heidelberg

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Lin, T.Y., Tremba, J. (2000). Attribute Transformations on Numerical Databases. In: Terano, T., Liu, H., Chen, A.L.P. (eds) Knowledge Discovery and Data Mining. Current Issues and New Applications. PAKDD 2000. Lecture Notes in Computer Science(), vol 1805. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45571-X_23

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  • DOI: https://doi.org/10.1007/3-540-45571-X_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67382-8

  • Online ISBN: 978-3-540-45571-4

  • eBook Packages: Springer Book Archive

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