Mining Frequent Bipartite Episode from Event Sequences

  • Authors:
  • Takashi Katoh;Hiroki Arimura;Kouichi Hirata

  • Affiliations:
  • Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814;Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan 060-0814;Department of Artificial Intelligence, Kyushu Institute of Technology, Iizuka, Japan 820-8502

  • Venue:
  • DS '09 Proceedings of the 12th International Conference on Discovery Science
  • Year:
  • 2009

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Abstract

In this paper, first we introduce a bipartite episode of the form A ***B 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|2 n ) 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.