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Disjunctive Sequential Patterns on Single Data Sequence and Its Anti-monotonicity

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3587))

Abstract

In this work, we proposes a novel method for mining frequent disjunctive patterns on single data sequence. For this purpose, we introduce a sophisticated measure that satisfies anti-monotonicity, by which we can discuss efficient mining algorithm based on APRIORI. We discuss some experimental results.

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Author information

Authors and Affiliations

  1. Dept.of Elect.& Elect. Engr., HOSEI University, 3-7-2 KajinoCho, Koganei, Tokyo, 184–8584, Japan

    Kazuhiro Shimizu & Takao Miura

Authors
  1. Kazuhiro Shimizu
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  2. Takao Miura
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Editor information

Editors and Affiliations

  1. Institute of Computer Vision and applied Computer Sciences, IBaI, Germany

    Petra Perner

  2. Institute of Media and Information Technology, Chiba University, Japan

    Atsushi Imiya

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© 2005 Springer-Verlag Berlin Heidelberg

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Shimizu, K., Miura, T. (2005). Disjunctive Sequential Patterns on Single Data Sequence and Its Anti-monotonicity. In: Perner, P., Imiya, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2005. Lecture Notes in Computer Science(), vol 3587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11510888_37

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  • DOI: https://doi.org/10.1007/11510888_37

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-26923-6

  • Online ISBN: 978-3-540-31891-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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