Projection-based partial periodic pattern mining for event sequences

Kung Jiuan Yang, Tzung Pei Hong, Yuh Min Chen, Guo Cheng Lan

Research output: Contribution to journalArticlepeer-review

19 Citations (Scopus)


Partial periodic pattern mining is one of the important issues in the field of data mining due to its practical applications. A partial periodic pattern consists of some periodic and non-periodic events in a specific period length, and is repeated with high frequency in an event sequence. In the past, a max-subpattern hit set algorithm was developed to discover partial periodic patterns, but its drawback is spending a large amount of time in calculating frequency counts from the redundant candidate nodes. In this study, we thus adopt an efficient encoding strategy to speed up the efficiency of processing period segments in an event sequence, and combined with the projection method to quickly find the partial periodic patterns in the recursive process. Finally, the experimental results show the superior performance of the proposed approach.

Original languageEnglish
Pages (from-to)4232-4240
Number of pages9
JournalExpert Systems With Applications
Issue number10
Publication statusPublished - 2013 Aug 1

All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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