Skip to main content
SpringerLink
Log in

Neural Method for Detection of Complex Patterns in Databases

  • Conference paper
  • First Online:
Methodologies for Knowledge Discovery and Data Mining (PAKDD 1999)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1574))

Included in the following conference series:

  • 1015 Accesses

Abstract

Databases storing real data contain complex patterns, such as chaotic pattern which generally are characteristics of greatly random fluctuation that often appear between deterministic and stochastic patterns of knowledge discovery in database. Chaotic patterns are always treated as random fluctuation distributions and ignored in literature so far. A novel network approach to discover and predict chaotic pattern in databases is proposed in this paper, which together with Zytkow’s Forty-Niner can not only discover the chaotic pattern but also predict it efficiently. In addition, this approach is very suitable to deal with large databases and has extensive applicable prospects in the vivid research fields of KDD.

This project was supported by the Natural Science Foundation of China, P. R. China

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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. M. Casdagli, “Nonlinear prediction of chaotic time series,” Physica D, vol. 35, pp. 335–356, 1989.

    Article  MATH  MathSciNet  Google Scholar 

  2. J.F. Elder IV, Daryl Pregibon, “A Statistical Perspective on Knowledge Discovery in Databases,” in Advances in Knowledge Discovery and Data Mining, U. Fayyad, G. Piatetsky-Shapiro, P. Smyth & R. Uthurusamy eds. AAAI press, 1996.

    Google Scholar 

  3. Jan M. Zytkow, “Database Exploration in Search of Regularities,” Journal of Intelligent Information Systems, 2, 39–81, 1993.

    Article  Google Scholar 

  4. Y. Lu, N, etc., “A Sequential Learning Scheme for Function Approximation Using Minimal Radial Basis Function Neural Networks,” Neural Computation 9, pp. 461–478, 1997.

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Institute of Intelligent Machines, Academia Sinica, University of Science and Technology of China, Hefei, 230027, P. R. China

    Chao Deng & Fanlun Xiong

Authors
  1. Chao Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fanlun Xiong
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Systems Engineering, Yamaguchi University, Tokiwa-Dai, 2557, Ube, 755, Japan

    Ning Zhong

  2. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Lizhu Zhou

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Deng, C., Xiong, F. (1999). Neural Method for Detection of Complex Patterns in Databases. In: Zhong, N., Zhou, L. (eds) Methodologies for Knowledge Discovery and Data Mining. PAKDD 1999. Lecture Notes in Computer Science(), vol 1574. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48912-6_35

Download citation

  • DOI: https://doi.org/10.1007/3-540-48912-6_35

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65866-5

  • Online ISBN: 978-3-540-48912-2

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation