Mining Temporal Patterns from Sequence Database of Interval-Based Events

  • Yen-Liang Chen
  • Shin-Yi Wu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


Sequential pattern mining is one of the important techniques of data mining to discover some potential useful knowledge from large databases. However, existing approaches for mining sequential patterns are designed for point-based events. In many applications, the essence of events are interval-based, such as disease suffered, stock price increase or decrease, chatting etc. This paper presents a new algorithm to discover temporal pattern from temporal sequences database consisting of interval-based events.


Temporal Pattern Event Type Sequential Pattern Temporal Sequence Pattern Mining 
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 2006

Authors and Affiliations

  • Yen-Liang Chen
    • 1
  • Shin-Yi Wu
    • 1
  1. 1.Department of Information ManagementNational Central UniversityChung-Li, TaiwanChina

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