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Compression-Based Measures for Mining Interesting Rules

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Next-Generation Applied Intelligence (IEA/AIE 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5579))

Abstract

An interestingness measure estimates the degree of interestingness of a discovered pattern and has been actively studied in the past two decades. Several pitfalls should be avoided in the study such as a use of many parameters and a lack of systematic evaluation in the presence of noise. Compression-based measures have advantages in this respect as they are typically parameter-free and robust to noise. In this paper, we present J-measure and a measure based on an extension of the Minimum Description Length Principle (MDLP) as compression-based measures for mining interesting rules.

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Author information

Authors and Affiliations

  1. Kyushu University, Fukuoka, 819-0395, Japan

    Einoshin Suzuki

Authors
  1. Einoshin Suzuki
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Information Engineering, National University of Tainan, 700, Tainan, Taiwan

    Been-Chian Chien

  2. Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan

    Tzung-Pei Hong

  3. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

  4. Department of Computer Science, Texas State University-San Marcos, 601 University Drive, 78666-4616, San Marcos, TX, USA

    Moonis Ali

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Suzuki, E. (2009). Compression-Based Measures for Mining Interesting Rules. In: Chien, BC., Hong, TP., Chen, SM., Ali, M. (eds) Next-Generation Applied Intelligence. IEA/AIE 2009. Lecture Notes in Computer Science(), vol 5579. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02568-6_75

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  • DOI: https://doi.org/10.1007/978-3-642-02568-6_75

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02567-9

  • Online ISBN: 978-3-642-02568-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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