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Efficient Algorithms for Mining Frequent and Closed Patterns from Semi-structured Data

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Advances in Knowledge Discovery and Data Mining (PAKDD 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5012))

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Abstract

In this talk, we study effcient algorithms that find frequent patterns and maximal (or closed) patterns from large collections of semi-structured data. We review basic techniques developed by the authors, called the rightmost expansion and the PPC-extension, respectively, for designing efficient frequent and maximal/closed pattern mining algorithms for large semi-structured data. Then, we discuss their applications to design of polynomial-delay and polynomial-space algorithms for frequent and maximal pattern mining of sets, sequences, trees, and graphs.

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Authors and Affiliations

  1. Hokkaido University, Kita 14-jo, Nishi 9-chome, Sapporo, 060-0814, Japan

    Hiroki Arimura

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  1. Hiroki Arimura
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Editor information

Takashi Washio Einoshin Suzuki Kai Ming Ting Akihiro Inokuchi

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Arimura, H. (2008). Efficient Algorithms for Mining Frequent and Closed Patterns from Semi-structured Data. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_2

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  • DOI: https://doi.org/10.1007/978-3-540-68125-0_2

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68124-3

  • Online ISBN: 978-3-540-68125-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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