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Discovering Most Classificatory Patterns for Very Expressive Pattern Classes

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Discovery Science (DS 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2843))

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Abstract

The classificatory power of a pattern is measured by how well it separates two given sets of strings. This paper gives practical algorithms to find the fixed/variable-length-don’t-care pattern (FVLDC pattern) and approximate FVLDC pattern which are most classificatory for two given string sets. We also present algorithms to discover the best window-accumulated FVLDC pattern and window-accumulated approximate FVLDC pattern. All of our new algorithms run in practical amount of time by means of suitable pruning heuristics and fast pattern matching techniques.

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Author information

Authors and Affiliations

  1. Department of Informatics, Kyushu University 33, Fukuoka, 812-8581, Japan

    Masayuki Takeda, Shunsuke Inenaga, Ayumi Shinohara & Setsuo Arikawa

  2. PRESTO, Japan Science and Technology Corporation (JST),  

    Masayuki Takeda, Shunsuke Inenaga & Ayumi Shinohara

  3. Human Genome Center, University of Tokyo, Tokyo, 108-8639, Japan

    Hideo Bannai

Authors
  1. Masayuki Takeda
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  2. Shunsuke Inenaga
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  3. Hideo Bannai
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  4. Ayumi Shinohara
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  5. Setsuo Arikawa
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Editor information

Editors and Affiliations

  1. FG Knowledge Engineering, FB Informatik, Technical University Darmstadt, Hochschulstr. 10, 64289, Darmstadt

    Gunter Grieser

  2. Meme Media Laboratory, Hokkaido University, N13 W8, 0608628, Sapporo, Japan

    Yuzuru Tanaka

  3. Graduate School of Informatics, Kyoto University Yoshida Honmachi, Sakyo-ku, 606-850, Kyoto, Japan

    Akihiro Yamamoto

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© 2003 Springer-Verlag Berlin Heidelberg

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Takeda, M., Inenaga, S., Bannai, H., Shinohara, A., Arikawa, S. (2003). Discovering Most Classificatory Patterns for Very Expressive Pattern Classes. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds) Discovery Science. DS 2003. Lecture Notes in Computer Science(), vol 2843. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39644-4_50

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  • DOI: https://doi.org/10.1007/978-3-540-39644-4_50

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20293-6

  • Online ISBN: 978-3-540-39644-4

  • eBook Packages: Springer Book Archive

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