Skip to main content
SpringerLink
Log in

Efficient Mining of Jumping Emerging Patterns with Occurrence Counts for Classification

  • Conference paper
Book cover Transactions on Rough Sets XIII

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 6499))

Abstract

In this paper we propose an efficient method of discovering Jumping Emerging Patterns with Occurrence Counts for the use in classification of data with numeric or nominal attributes. This new extension of Jumping Emerging Patterns proved to perform well when classifying image data and here we experimentally compare it to other methods, by using generalized border-based pattern mining algorithm to build the classifier.

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. Dong, G., Li, J.: Efficient mining of emerging patterns: Discovering trends and differences. In: KDD 1999: Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 43–52. ACM, New York (1999)

    Google Scholar 

  2. Dong, G., Zhang, X., Wong, L., Li, J.: CAEP: Classification by aggregating emerging patterns. In: Arikawa, S., Furukawa, K. (eds.) DS 1999. LNCS (LNAI), vol. 1721, pp. 30–42. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  3. Dong, G., Li, J.: Mining border descriptions of emerging patterns from dataset pairs. Knowledge and Information Systems 8(2), 178–202 (2005)

    Article  Google Scholar 

  4. Li, J., Dong, G., Ramamohanarao, K.: Instance-based classification by emerging patterns. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 191–200. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  5. Li, J., Dong, G., Ramamohanarao, K., Wong, L.: DeEPs: A new instance-based lazy discovery and classification system. Machine Learning 54(2), 99–124 (2004)

    Article  MATH  Google Scholar 

  6. Li, J., Dong, G., Ramamohanarao, K.: Making use of the most expressive jumping emerging patterns for classification. Knowledge and Information Systems 3(2), 1–29 (2001)

    MATH  Google Scholar 

  7. Li, J., Ramamohanarao, K., Dong, G.: The space of jumping emerging patterns and its incremental maintenance algorithms. In: ICML 2000: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 551–558. Morgan Kaufmann Publishers Inc., San Francisco (2000)

    Google Scholar 

  8. Fan, H., Ramamohanarao, K.: An efficient single-scan algorithm for mining essential jumping emerging patterns for classification. In: Chen, M.-S., Yu, P.S., Liu, B. (eds.) PAKDD 2002. LNCS (LNAI), vol. 2336, pp. 456–462. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  9. Fan, H., Ramamohanarao, K.: Fast discovery and the generalization of strong jumping emerging patterns for building compact and accurate classifiers. IEEE Transactions on Knowledge and Data Engineering 18(6), 721–737 (2006)

    Article  Google Scholar 

  10. Terlecki, P., Walczak, K.: On the relation between rough set reducts and jumping emerging patterns. Information Sciences 177(1), 74–83 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  11. Li, J., Liu, G., Wong, L.: Mining statistically important equivalence classes and delta-discriminative emerging pattern. In: Proceedings of 13th International Conference on Knowledge Discovery and Data Mining, San Jose, California, pp. 430–439 (2007)

    Google Scholar 

  12. Li, J., Wong, L.: Emerging patterns and gene expression data. Genome Informatics 12, 3–13 (2001)

    Google Scholar 

  13. Li, J., Wong, L.: Identifying good diagnostic gene groups from gene expression profiles using the concept of emerging patterns. Bioinformatics 18, 725–734 (2002)

    Article  Google Scholar 

  14. Zaïane, O.R., Han, J., Zhu, H.: Mining recurrent items in multimedia with progressive resolution refinement. In: Proceedings of the 16th International Conference on Data Engineering, San Diego, CA, USA, pp. 461–470 (2000)

    Google Scholar 

  15. Ong, K.-L., Ng, W.-K., Lim, E.-P.: Mining multi-level rules with recurrent items using FP’-Tree. In: Proceedings of the Third International Conference on Information, Communications and Signal Processing (2001)

    Google Scholar 

  16. Rak, R., Kurgan, L.A., Reformat, M.: A tree-projection-based algorithm for multi-label recurrent-item associative-classification rule generation. Data and Knowledge Engineering 64(1), 171–197 (2008)

    Article  Google Scholar 

  17. Kobyliński, Ł., Walczak, K.: Jumping emerging patterns with occurrence count in image classification. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 904–909. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  18. Wang, J.Z., Li, J., Wiederhold, G.: SIMPLIcity: Semantics-sensitive integrated matching for picture libraries. IEEE Trans. on Patt. Anal. and Machine Intell. 23, 947–963 (2001)

    Article  Google Scholar 

  19. Witten, I.H., Frank, E.: Data Mining: Practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, San Francisco (2005)

    MATH  Google Scholar 

  20. Lewis, D.D., Williams, K.: Reuters-21578 corpus ApteMod version

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Computer Science, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665, Warszawa, Poland

    Łukasz Kobyliński & Krzysztof Walczak

Authors
  1. Łukasz Kobyliński
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krzysztof Walczak
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Manitoba, R3T 5V6, Winnipeg, Manitoba, Canada

    James F. Peters

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

    Andrzej Skowron

  3. Department of Computer Science, The University of Akron, 44325-4003, Akron, OH, USA

    Chien-Chung Chan

  4. Department of Electrical Engineering and Computer Science, The University of Kansas, 66045, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

  5. Department of Computer Science, University of Regina, SK S4S 0A2, Regina, Canada

    Wojciech P. Ziarko

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Kobyliński, Ł., Walczak, K. (2011). Efficient Mining of Jumping Emerging Patterns with Occurrence Counts for Classification. In: Peters, J.F., Skowron, A., Chan, CC., Grzymala-Busse, J.W., Ziarko, W.P. (eds) Transactions on Rough Sets XIII. Lecture Notes in Computer Science, vol 6499. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-18302-7_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-18302-7_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-18301-0

  • Online ISBN: 978-3-642-18302-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation