Robust Mining of Time Intervals with Semi-interval Partial Order Patterns

@inproceedings{Mrchen2010RobustMO,
  title={Robust Mining of Time Intervals with Semi-interval Partial Order Patterns},
  author={Fabian M{\"o}rchen and Dmitriy Fradkin},
  booktitle={SDM},
  year={2010}
}
We present a new approach to mining patterns from symbolic interval data that extends previous approaches by allowing semi-intervals and partially ordered patterns. The mining algorithm combines and adapts efficient algorithms from sequential pattern and itemset mining for discovery of the new semi-interval patterns. The semi-interval patterns and semi-interval partial order patterns are more flexible than patterns over full intervals, and are empirically demonstrated to be more useful as… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-10 OF 43 REFERENCES

Efficient mining of understandable patterns from multivariate interval time series

  • Data Mining and Knowledge Discovery
  • 2007
VIEW 13 EXCERPTS
HIGHLY INFLUENTIAL

Mining Nonambiguous Temporal Patterns for Interval-Based Events

  • IEEE Transactions on Knowledge and Data Engineering
  • 2007
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

BIDE: efficient mining of frequent closed sequences

  • Proceedings. 20th International Conference on Data Engineering
  • 2004
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Maintaining Knowledge about Temporal Intervals

  • Commun. ACM
  • 1981
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Karmalego - fast time intervals mining

R. Moskovitch, Y. Shahar
  • Technical Report 23, ISE-TECH- REP Ben Gurion University,
  • 2009
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Fast and memory efficient mining of frequent closed itemsets

  • IEEE Transactions on Knowledge and Data Engineering
  • 2006
VIEW 4 EXCERPTS
HIGHLY INFLUENTIAL

Discovering frequent arrangements of temporal intervals

  • Fifth IEEE International Conference on Data Mining (ICDM'05)
  • 2005
VIEW 10 EXCERPTS
HIGHLY INFLUENTIAL

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