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Efficient Mining of Jumping Emerging Patterns with Occurrence Counts for Classification

  • Conference paper
Transactions on Rough Sets XIII

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

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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.

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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
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  2. Krzysztof Walczak
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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

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© 2011 Springer-Verlag Berlin Heidelberg

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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

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  • 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

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