Mining Sectorial Episodes from Event Sequences

  • Takashi Katoh
  • Kouichi Hirata
  • Masateru Harao
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4265)


In this paper, we introduce a sectorial episode of the form Cr, where C is a set of events and r is an event. The sectorial episode Cr means that every event of C is followed by an event r. Then, by formulating the support and the confidence of sectorial episodes, in this paper, we design the algorithm Sect to extract all of the sectorial episodes that are frequent and accurate from a given event sequence by traversing it just once. Finally, by applying the algorithm Sect to bacterial culture data, we extract sectorial episodes representing drug-resistant change.


Association Rule Space Complexity Event Type Event Sequence Sequential Pattern 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Takashi Katoh
    • 1
  • Kouichi Hirata
    • 2
  • Masateru Harao
    • 2
  1. 1.Graduate School of Computer Science and Systems Engineering 
  2. 2.Department of Artificial IntelligenceKyushu Institute of TechnologyIizukaJapan

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