Mining Frequent Bipartite Episode from Event Sequences

  • Takashi Katoh
  • Hiroki Arimura
  • Kouichi Hirata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5808)


In this paper, first we introduce a bipartite episode of the form AB for two sets A and B of events, which means that every event of A is followed by every event of B. Then, we present an algorithm that finds all frequent bipartite episodes from an input sequence without duplication in O(|Σ| ·N) time per an episode and in O(|Σ|2n) space, where Σ is an alphabet, N is total input size of \(\mathcal S\), and n is the length of S. Finally, we give experimental results on artificial and real sequences to evaluate the efficiency of the algorithm.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Takashi Katoh
    • 1
  • Hiroki Arimura
    • 1
  • Kouichi Hirata
    • 2
  1. 1.Graduate School of Information Science and TechnologyHokkaido UniversitySapporoJapan
  2. 2.Department of Artificial IntelligenceKyushu Institute of TechnologyIizukaJapan

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