Efficient mining strategy for frequent serial episodes in temporal database

  • Authors:

  • Kuo-Yu Huang;Chia-Hui Chang

  • Affiliations:

  • Department of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan;Department of Computer Science and Information Engineering, National Central University, Chung-Li, Taiwan

  • Venue:
  • APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
  • Year:
  • 2006

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Abstract

Discovering patterns with great significance is an important problem in data mining discipline. A serial episode is defined to be a partially ordered set of events for consecutive and fixed-time intervals in a sequence. Previous studies on serial episodes consider only frequent serial episodes in a sequence of events (called simple sequence). In real world, we may find a set of events at each time slot in terms of various intervals (called complex sequence). Mining frequent serial episodes in complex sequences has more extensive applications than that in simple sequences. In this paper, we discuss the problem on mining frequent serial episodes in a complex sequence. We extend previous algorithm MINEPI to MINEPI+ for serial episode mining from complex sequences. Furthermore, a memory-anchored algorithm called EMMA is introduced for the mining task.