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Mining Frequent k-Partite Episodes from Event Sequences

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New Frontiers in Artificial Intelligence (JSAI-isAI 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6284))

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Abstract

In this paper, we introduce the class of k-partite episodes, which are time-series patterns of the form 〈A 1,...,A k〉 for sets A i (1 ≤ i ≤ k) of events meaning that, in an input event sequence, every event of A i is followed by every event of A i + 1 for every 1 ≤ i < k. Then, we present a backtracking algorithm Kpar and its modification Kpar2 that find all of the frequent k-partite episodes from an input event sequence without duplication. By theoretical analysis, we show that these two algorithms run in polynomial delay and polynomial space in total input size.

This work is partially supported by Grand-in-Aid for JSPS Fellows (20·3406).

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Author information

Authors and Affiliations

  1. Graduate School of Information Science and Technology, Hokkaido University, Kita 14-jo Nishi 9-chome, Sapporo, 060-0814, Japan

    Takashi Katoh & Hiroki Arimura

  2. Department of Artificial Intelligence, Kyushu Institute of Technology, Kawazu 680-4, Iizuka, 820-8502, Japan

    Kouichi Hirata

Authors
  1. Takashi Katoh
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  2. Hiroki Arimura
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  3. Kouichi Hirata
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Editor information

Editors and Affiliations

  1. SRA Inc., Key Technology Laboratory, 2-32-8 Minami-Ikebukuro, Toyoshima-Ku, 171-8513, Tokyo, Japan

    Kumiyo Nakakoji

  2. National Institute of Information and Communications Technology, 3-5 Hikaridai, Seika-cho, Soraku-gun, 619-0289, Kyoto, Japan

    Yohei Murakami

  3. Department of English, Aoyama Gakuin University, 4-4-25 Shibuya, Shibuya-ku, 150-8366, Tokyo, Japan

    Elin McCready

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Katoh, T., Arimura, H., Hirata, K. (2010). Mining Frequent k-Partite Episodes from Event Sequences. In: Nakakoji, K., Murakami, Y., McCready, E. (eds) New Frontiers in Artificial Intelligence. JSAI-isAI 2009. Lecture Notes in Computer Science(), vol 6284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14888-0_26

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  • DOI: https://doi.org/10.1007/978-3-642-14888-0_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14887-3

  • Online ISBN: 978-3-642-14888-0

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