Mining Sequences of Temporal Intervals

  • Steffen Kempe
  • Jochen Hipp
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


Recently a new type of data source came into the focus of knowledge discovery from temporal data: interval sequences. In contrast to event sequences, interval sequences contain labeled events with a temporal extension. However, existing algorithms for mining patterns from interval sequences proved to be far from satisfying our needs. In brief, we missed an approach that at the same time: defines support as the number of pattern instances, allows input data that consists of more than one sequence, implements time constraints on a pattern instance, and counts multiple instances of a pattern within one interval sequence. In this paper we propose a new support definition which incorporates these properties. We also describe an algorithm that employs the new support definition and demonstrate its performance on field data from the automotive business.


Temporal Pattern State Machine Temporal Interval Frequent Pattern Multiple Instance 
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

  • Steffen Kempe
    • 1
  • Jochen Hipp
    • 1
  1. 1.DaimlerChrysler AG, Group ResearchUlmGermany

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