Skip to main content

Statistically Significant Patterns

  • Chapter
  • 593 Accesses

Part of the book series: Advances in Database Systems ((ADBS,volume 28))

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., and Srikant, R. (1994). Fast algorithms for mining association rules in large databases. Proc. of the Int'l Conference on Very Larg Databases (VLDB). pp. 487–499.

    Google Scholar 

  2. Agrawal, R.. and Srikant, R. (1995). Mininig sequential patterns. Proc. of the Int'l Conference on Data Engineering (ICDE). pp. 3–14.

    Google Scholar 

  3. Berger, G., and Tuzhilin, A. (1998). Discovering unexpected patterns in temporal data using temporal logic. Temporal Databases — Research and Practice, Lecture Notes on Computer Sciences. vol. (1399) pp. 281–309.

    Google Scholar 

  4. Bentley, J. (1984). Programming pearls. Communications of ACM. (27) 2:865–871.

    Article  Google Scholar 

  5. Blahut, R. (1987). Principles and Practice of Information Theory. Addison-Wesley Publishing Company.

    Google Scholar 

  6. Brin. S., Motwani, R., and Silverstein, C. (1997). Beyond market baskets: generalizing association rules to correlations. Proc. ACM SIGMOD Int'l. Conference on Management of Data (SIGMOD). pp. 265–276.

    Google Scholar 

  7. Califano, A., Stolovitzky, G., and Tu, Y. (1999) Analysis of gene expression microarrays: a combinatorial multivariate approach, IBM T. J. Watson Research Report.

    Google Scholar 

  8. Chakrabarti, S., Sarawagi, S., and Dom, B. (1998). Mining surprising patterns using temporal description length. Proc. Int. Conf. on Very Large Data Bases (VLDB). pp. 606–617.

    Google Scholar 

  9. Cohen, E., Datar, M., Fuijiwara, S., Cionis, A., Indyk, P., Motwani, R., Ullman, J., and Yang, C. (2000). Finding interesting associations without support pruning. Proc. 16th IEEE Int'l. Conference on Data Engineering (ICDE). pp. 489–499.

    Google Scholar 

  10. Fukuda, T., Morimoto, Y., Morishita, S., and Tokuyama, T. (1996). Mining optimized association rules for numeric attributes. Proc. 15th ACM Symposium on Principles of Database Systems (PODS). pp. 182–191.

    Google Scholar 

  11. Han, J., Gong, W., and Yin, Y. (1998). Mining segment-wise periodic patterns in time-related databases. Proc. Int'l. Conference on Knowledge Discovery and Data Mining (KDD). pp. 214–218.

    Google Scholar 

  12. Han, J., Dong, G., and Yin, Y. (1999). Efficient mining partial periodic patterns in time series database. Proc. IEEE Int'l. Conference on Data Engineering (ICDE). pp. 106–115.

    Google Scholar 

  13. Liu, B., Hsu, W., and Ma, Y. (1999) Mining association Rules with multiple minimum supports. Proc. Int'l. Conference on Knowledge Discovery and Data Mining (KDD). pp. 337–341.

    Google Scholar 

  14. Mannila, H., Pavlov, D., and Smyth, P. (1999). Prediction with local patterns using cross-entropy. Proc. Int'l. Conference on Knowledge Discovery and Data Mining (KDD). pp. 357–361.

    Google Scholar 

  15. Oates, T., Schmill, M., and Cohen, P. (1999). Efficient mining of statistical dependencies. Proc. 16th Int. Joint Conf. on Artificial Intelligence. pp. 794–799.

    Google Scholar 

  16. Ozden, B., Ramaswamy, S., and Silberschatz, A. (1998). Cyclic association rules. Proc. 14th Int'l. Conference on Data Engineering (ICDE). pp. 412–421.

    Google Scholar 

  17. Silberschatz, A., and Tuzhilin, A. (1996). What makes patterns interesting in knowledge discover systems. IEEE Transactions on Knowledge and Data Engineering (TKDE). vol. 8 no. 6, pp. 970–974.

    Article  Google Scholar 

  18. Yang, J. Wang, W., and Yu, P. (2001). Infominer: mining surprising periodic patterns. Proc. of the Seventh ACM Int'l Conference on Knowledge Discover and Data Mining (KDD). pp. 395–400.

    Google Scholar 

  19. Yang, J. Wang, W., and Yu, P. (2003). STAMP: on discovery of statistically important pattern repeats in long sequential data. Proc. of the Third SIAM International Conference on Data Mining (SDM).

    Google Scholar 

  20. Yang, J., Wang, W., and Yu, P. (2003). Mining asynchronous periodic patterns in time series data. IEEE Transactions on Knowledge and Data Engineering. 15(3):613–628.

    Article  Google Scholar 

  21. Zaki, M. (2000). Generating non-redundant association rules. Proc. of the Sixth ACM Int'l Conference on Knowledge Discover and Data Mining (KDD). pp. 34–43.

    Google Scholar 

Download references

Rights and permissions

Reprints and permissions

Copyright information

© 2005 Springer Science+Business Media, Inc.

About this chapter

Cite this chapter

(2005). Statistically Significant Patterns. In: Mining Sequential Patterns from Large Data Sets. Advances in Database Systems, vol 28. Springer, Boston, MA. https://doi.org/10.1007/0-387-24247-3_4

Download citation

  • DOI: https://doi.org/10.1007/0-387-24247-3_4

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-24246-0

  • Online ISBN: 978-0-387-24247-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics