Chapter

Discovery Science

Volume 5808 of the series Lecture Notes in Computer Science pp 136-151

Mining Frequent Bipartite Episode from Event Sequences

  • Takashi KatohAffiliated withGraduate School of Information Science and Technology, Hokkaido University
  • , Hiroki ArimuraAffiliated withGraduate School of Information Science and Technology, Hokkaido University
  • , Kouichi HirataAffiliated withDepartment of Artificial Intelligence, Kyushu Institute of Technology

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Abstract

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|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.