Efficient mining of frequent episodes from complex sequences

Kuo Yu Huang, Chia Hui Chang

Research output: Contribution to journalArticlepeer-review

68 Scopus citations

Abstract

Discovering patterns with great significance is an important problem in data mining discipline. An episode is defined to be a partially ordered set of events for consecutive and fixed-time intervals in a sequence. Most of previous studies on episodes consider only frequent 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 (hours, days, weeks, etc.). We refer to such sequences as complex sequences. Mining frequent episodes in complex sequences has more extensive applications than that in simple sequences. In this paper, we discuss the problem on mining frequent episodes in a complex sequence. We extend previous algorithm MINEPI to MINEPI + for episode mining from complex sequences. Furthermore, a memory-anchored algorithm called EMMA is introduced for the mining task. Experimental evaluation on both real-world and synthetic data sets shows that EMMA is more efficient than MINEPI +.

Original languageEnglish
Pages (from-to)96-114
Number of pages19
JournalInformation Systems
Volume33
Issue number1
DOIs
StatePublished - Mar 2008

Keywords

  • Data mining
  • Frequent episodes
  • Temporal association

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