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Mining Frequent k-Partite Episodes from Event Sequences

  • Takashi Katoh
  • Hiroki Arimura
  • Kouichi Hirata
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6284)

Abstract

In this paper, we introduce the class of k-partite episodes, which are time-series patterns of the form 〈A 1,...,A k 〉 for sets A i (1 ≤ i ≤ k) of events meaning that, in an input event sequence, every event of A i is followed by every event of A i + 1 for every 1 ≤ i < k. Then, we present a backtracking algorithm Kpar and its modification Kpar2 that find all of the frequent k-partite episodes from an input event sequence without duplication. By theoretical analysis, we show that these two algorithms run in polynomial delay and polynomial space in total input size.

Keywords

Input Sequence Event Sequence Window Width Polynomial Space Mining 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 2010

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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