Discovering multi-label temporal patterns in sequence databases

Yen Liang Chen, Shin Yi Wu, Yu Cheng Wang

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Sequential pattern mining is one of the most important data mining techniques. Previous research on mining sequential patterns discovered patterns from point-based event data, interval-based event data, and hybrid event data. In many real life applications, however, an event may involve many statuses; it might not occur only at one certain point in time or over a period of time. In this work, we propose a generalized representation of temporal events. We treat events as multi-label events with many statuses, and introduce an algorithm called MLTPM to discover multi-label temporal patterns from temporal databases. The experimental results show that the efficiency and scalability of the MLTPM algorithm are satisfactory. We also discuss interesting multi-label temporal patterns discovered when MLTPM was applied to historical Nasdaq data.

Original languageEnglish
Pages (from-to)398-418
Number of pages21
JournalInformation Sciences
Issue number3
StatePublished - 1 Feb 2011


  • Interval-based event sequence
  • Point-based event sequence
  • Sequential patterns
  • Temporal patterns


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