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Wavelet Methods in Data Mining

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Data Mining and Knowledge Discovery Handbook

Summary

Recently there has been significant development in the use of wavelet methods in various Data Mining processes. This article presents general overview of their applications in Data Mining. It first presents a high-level data-mining framework in which the overall process is divided into smaller components. It reviews applications of wavelets for each component. It discusses the impact of wavelets on Data Mining research and outlines potential future research directions and applications.

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Author information

Authors and Affiliations

  1. School of Computer Science Florida International University, Miami, FL, 33199, USA

    Tao Li

  2. Machine Learning for Systems, IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY, 10532, USA

    Sheng Ma

  3. Computer Science Department, University of Rochester, Rochester, NY, 14627-0226, USA

    Mitsunori Ogihara

Authors
  1. Tao Li
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  2. Sheng Ma
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  3. Mitsunori Ogihara
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Corresponding author

Correspondence to Tao Li .

Editor information

Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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© 2009 Springer Science+Business Media, LLC

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Li, T., Ma, S., Ogihara, M. (2009). Wavelet Methods in Data Mining. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_27

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_27

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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