Skip to main content
SpringerLink
Log in

A Practical Algorithm to Find the Best Episode Patterns

  • Conference paper
  • First Online:
Discovery Science (DS 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2226))

Included in the following conference series:

Abstract

Episode pattern is a generalized concept of subsequence pattern where the length of substring containing the subsequence is bounded. Given two sets of strings, consider an optimization problem to find a best episode pattern that is common to one set but not common in the other set. The problem is known to be NP-hard. We give a practical algorithm to solve it exactly.

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. D. Angluin. Finding patterns common to a set of strings. J. Comput. Syst. Sci., 21(1):46–62, Aug. 1980.

    Article  MATH  MathSciNet  Google Scholar 

  2. R. A. Baeza-Yates. Searching subsequences. Theoretical Computer Science, 78(2):363–376, Jan. 1991.

    Article  MATH  MathSciNet  Google Scholar 

  3. R. Fujino, H. Arimura, and S. Arikawa. Discovering unordered and ordered phrase association patterns for text mining. In Proc. of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, volume 1805 of Lecture Notes in Artificial Intelligence. Springer-Verlag, Apr. 2000.

    Google Scholar 

  4. M. Hirao, H. Hoshino, A. Shinohara, M. Takeda, and S. Arikawa. A practical algorithm to find the best subsequence patterns. In Proc. of The Third International Conference on Discovery Science, volume 1967 of Lecture Notes in Artificial Intelligence, pages 141–154. Springer-Verlag, Dec. 2000.

    Google Scholar 

  5. H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering frequent episode in sequences. In U. M. Fayyad and R. Uthurusamy, editors, Proc. of the 1st International Conference on Knowledge Discovery and Data Mining, pages 210–215. AAAI Press, Aug. 1995.

    Google Scholar 

  6. S. Miyano, A. Shinohara, and T. Shinohara. Polynomial-time learning of elementary formal systems. New Generation Computing, 18:217–242, 2000.

    Article  Google Scholar 

  7. S. Shimozono, A. Shinohara, T. Shinohara, S. Miyano, S. Kuhara, and S. Arikawa. Knowledge acquisition from amino acid sequences by machine learning system BONSAI. Transactions of Information Processing Society of Japan, 35(10):2009–2018, Oct. 1994.

    Google Scholar 

  8. Z. Troníček. Episode matching. In Proc. of 12th Annual Symposium on Combinatorial Pattern Matching, Lecture Notes in Computer Science, pages 143–146. Springer-Verlag, July 2001.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, Kyushu University 33, 812-8581, Fukuoka, Japan

    Masahiro Hirao, Shunsuke Inenaga, Ayumi Shinohara, Masayuki Takeda & Setsuo Arikawa

Authors
  1. Masahiro Hirao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shunsuke Inenaga
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ayumi Shinohara
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Masayuki Takeda
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Setsuo Arikawa
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. DFKI GmbH Saarbrücken, 66123, Saarbrücken, Germany

    Klaus P. Jantke

  2. Department of Informatics, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, 812-8581, Fukuoka, Japan

    Ayumi Shinohara

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hirao, M., Inenaga, S., Shinohara, A., Takeda, M., Arikawa, S. (2001). A Practical Algorithm to Find the Best Episode Patterns. In: Jantke, K.P., Shinohara, A. (eds) Discovery Science. DS 2001. Lecture Notes in Computer Science(), vol 2226. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45650-3_37

Download citation

  • DOI: https://doi.org/10.1007/3-540-45650-3_37

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42956-2

  • Online ISBN: 978-3-540-45650-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation