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Finding Top-N Chance Patterns with KeyGraph\(^{\tiny \textregistered}\)-Based Importance

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Knowlege-Based and Intelligent Information and Engineering Systems (KES 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6882))

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Abstract

In this paper, as our first proposal, we discuss a method for finding a rare pattern, called a chance pattern, which connects a pair of more frequent patterns. Particularly, our chance pattern is defined with a KeyGraph \(^{\tiny \textregistered}\)-based importance of patterns. More concretely speaking, a chance pattern is a pattern C which often appears in a part of documents containing a frequent pattern X L as well as in those containing another pattern X R , that is, confidence values of association rules, X L ⇒C and X R ⇒C, are relatively high. It would be expected that such a chance pattern C reveals a hidden and implicit relationships between X L and X R . We design clique-search-based algorithms for finding chance patterns with Top-N confidence values.

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Author information

Authors and Affiliations

  1. Graduate School of Information Science and Technology, Hokkaido University, N-14 W-9, Sapporo, 060-0814, Japan

    Yoshiaki Okubo & Makoto Haraguchi

  2. Research Institute for Information Technology, Kyushu University, Fukuoka, 812-8581, Japan

    Sachio Hirokawa

Authors
  1. Yoshiaki Okubo
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  2. Makoto Haraguchi
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  3. Sachio Hirokawa
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Editor information

Editors and Affiliations

  1. Institute of Integrated Sensor Systems, University of Kaiserslautern, Erwin-Schroedinger-str. 12, 67663, Kaiserslautern, Germany

    Andreas König

  2. Knowledge-Based Systems Group, Department of Computer Science, University of Kaiserslautern, P.O. Box 3049, 67653, Kaiserslautern, Germany

    Andreas Dengel

  3. School of Business, University of Applied Sciences Northwestern Switzerland, Riggenbachstr. 16, 4600, Olten, Switzerland

    Knut Hinkelmann

  4. Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, 599-8531, Sakai,, Osaka, Japan

    Koichi Kise

  5. KES International, P.O. Box 2115, BN43 9AF, Shoreham-by-sea, UK

    Robert J. Howlett

  6. University of South Australia, Adelaide, 5095, Mawson Lakes, SA, Australia

    Lakhmi C. Jain

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© 2011 Springer-Verlag Berlin Heidelberg

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Okubo, Y., Haraguchi, M., Hirokawa, S. (2011). Finding Top-N Chance Patterns with KeyGraph\(^{\tiny \textregistered}\)-Based Importance. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds) Knowlege-Based and Intelligent Information and Engineering Systems. KES 2011. Lecture Notes in Computer Science(), vol 6882. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23863-5_47

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  • DOI: https://doi.org/10.1007/978-3-642-23863-5_47

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23862-8

  • Online ISBN: 978-3-642-23863-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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