Date: Wed May 27 22:50:03 2009

Title: Mining Frequent Bipartite Episode from Event Sequences

Authors: Takashi Katoh, Hiroki Arimura, and Kouichi Hirata


  • First name: Hiroki
  • Last name: Arimura
  • Address: Division of Computer Science, Graduate School of Information Science and Technology, Hokkaido University N14 W9, 060-0814 Sapporo, Japan
  • Email:

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 Bipar that finds all frequent bipartite episodes from an input sequence without duplication in O(|\Sigma| ||S||) time per an episode and in O(|\Sigma|^2 n) space, where \Sigma is an alphabet, || S || is total input size, and n is the length of the input sequence. Finally, we give experimental results on artificial sequences with varying several parameters to evaluate the efficiency of the algorithm.

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