Skip to main content
SpringerLink
Log in

Patterns in Numerical Data: Practical Approximations to Kolmogorov Complexity

  • Conference paper
New Directions in Rough Sets, Data Mining, and Granular-Soft Computing (RSFDGrC 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1711))

Abstract

Intuitively, patterns of numerical sequences are often interpreted as formulas. However, we observed earlier that such an intuition is too naive. Notions analogous to Kolmogorov complexity theory are introduced. Based on these new formulations, a formula is a pattern only if its pattern complexity is simpler than the complexity of data.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Birkhoff, G., Mac Lane, S.: A Survey of Modern Algebra Forth Ed. Macmillan Publising Co., New York (1977)

    Google Scholar 

  2. Domingos, P.: Occam’s Two Razors: The Sharp and the Blunt. In: Agrawal, R., Stolorz, P., Piatetsky-Shapiro, G. (eds.) The Proceedings of the Fourth International Conference on Knowledge Discovery & Data Mining, New York City, August 27-31 (1998)

    Google Scholar 

  3. Lin, T.Y.: Discovering Patterns in Numerical Sequences Using Rough Set Theory. In: The Proceeding of the Third World Multi-conference on Systemics, Cybernetics and Informatics, Orlando, Florida, July 31-August 4. Computer Science and Engineering, vol. 5 (1999)

    Google Scholar 

  4. Li, M., Vitanyi, P.M.: Komogorov Complexity and its Applications. Handbook of Theoretical Computer Science J. van Leeuwen

    Google Scholar 

  5. Dietterich, T.G., Michalski, R.S.: Learning to Predict Sequences. In: Micgalski, R., Carbonell, J., Michell, T. (eds.) Machine Learning: An Artificial Intelligeve Approach, vol. II, pp. 63–106. Morgan Kaufmann, San Franciscom

    Google Scholar 

  6. Mukherjea, A., Pothoven, K.: Real and Functional Analysis. Plenum Press, New York (1978)

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

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

    T. Y. Lin

  2. Department of Electrical Engineering and Computer Science, University of California, Berkeley, California, 94720

    T. Y. Lin

Authors
  1. T. Y. Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. The International WIC Institute, Beijing University of Technology, China

    Ning Zhong

  2. Institute of Mathematics, Warsaw University, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

  3. Professor Emeritus, University of Tokyo,  

    Setsuo Ohsuga

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lin, T.Y. (1999). Patterns in Numerical Data: Practical Approximations to Kolmogorov Complexity. In: Zhong, N., Skowron, A., Ohsuga, S. (eds) New Directions in Rough Sets, Data Mining, and Granular-Soft Computing. RSFDGrC 1999. Lecture Notes in Computer Science(), vol 1711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-48061-7_62

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-48061-7_62

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66645-5

  • Online ISBN: 978-3-540-48061-7

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation