Abstract
In this paper, we introduce a diamond episode of the form s 1↦E↦s 2, where s 1 and s 2 are events and E is a set of events. The diamond episode s 1↦E↦s 2 means that every event of E follows an event s 1 and is followed by an event s 2. Then, by formulating the support of diamond episodes, in this paper, we design the algorithm FreqDmd to extract all of the frequent diamond episodes from a given event sequence. Finally, by applying the algorithm FreqDmd to bacterial culture data, we extract diamond episodes representing replacement of bacteria.
This work is partially supported by Grand-in-Aid for Scientific Research 17200011 from the Ministry of Education, Culture, Sports, Science and Technology, Japan.
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Katoh, T., Hirata, K., Harao, M. (2007). Mining Frequent Diamond Episodes from Event Sequences . In: Torra, V., Narukawa, Y., Yoshida, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2007. Lecture Notes in Computer Science(), vol 4617. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73729-2_45
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DOI: https://doi.org/10.1007/978-3-540-73729-2_45
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