The computational complexity of high-dimensional correlation search

@article{Jermaine2001TheCC,
  title={The computational complexity of high-dimensional correlation search},
  author={Chris Jermaine},
  journal={Proceedings 2001 IEEE International Conference on Data Mining},
  year={2001},
  pages={249-256}
}
There is a growing awareness that the popular support metric (often used to guide search in market-basket analysis) is not appropriate for use in every association mining application. Support measures only the co-occurrence frequency of a set of events when determining which patterns to report back to the user. It incorporates no rigorous statistical notion of surprise or interest, and many of the patterns deemed interesting by the support metric are uninteresting to the user. However, a… CONTINUE READING

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References

Publications referenced by this paper.
SHOWING 1-4 OF 4 REFERENCES

Finding interesting associations without support pruning

Edith Cohen, Mayur Datar, +5 authors Cheng Yang
  • Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073)
  • 2000

The Computational Complexity of High-Dimensional Correlation Search

Christopher Jermaine
  • Queries Efficiently
  • 1998