Skip to main content

Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality

  • Chapter
Quality Measures in Data Mining

Part of the book series: Studies in Computational Intelligence ((SCI,volume 43))

  • 1143 Accesses

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Jorge A. Hierarchical clustering for thematic browsing and summarization of large sets of association rules. In Proceedings of the 2004 SIAM International Conference on Data Mining, 2004.

    Google Scholar 

  2. Kosters W. A., Marchiori E., and Oerlemans A. J. Mining clusters with association rules. In Proceedings of Third Symposium on Intelligent Data Analysis (IDA 99), volume 1642 of LNCS, pages 39-50. Springer-Verlag, 1999.

    Google Scholar 

  3. Baesens B., Viaene S., and Vanthienen J. Post-processing of association rules. In Proceedings of the Workshop on Post-Processing in Machine Learning and Data Mining: Interpretation, Visualization, Integration, and Related Topics within The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Boston, MA, USA, 2000. ACM SIGKDD.

    Google Scholar 

  4. Lent B., Swami A., and Widom J. Clustering association rules. In Proceedings of the Thirteenth International Conference on Data Engineering, pages 220-231, April 1997.

    Google Scholar 

  5. Liu B., Hu M., and Hsu W. Multi-level organization and summarization of the discovered rules. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2000), pages 208-217, Boston, USA, 2000. ACM SIGKDD, ACM Press.

    Google Scholar 

  6. Padmanabhan B. and Tuzhilin A. Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Systems, 27(3):303-318, 1999.

    Article  Google Scholar 

  7. Aggarwal C. C., Procopiuc C., and Yu P. S. Finding localized associations in market basket data. IEEE Transactions on Knowledge and Data Engineering, 14 (1):51-62, 2002.

    Article  Google Scholar 

  8. Adomavicius G. and Tuzhilin A. Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery, 5(1-2):33-58, 2001.

    Article  MATH  Google Scholar 

  9. Dong G. and Li J. Interestingness of discovered association rules in terms of neighborhood-based unexpectedness. In Proceedings of the Second Pacific-Asia Conference on Knowledge Discovery and Data Mining, pages 72-86. SpringerVerlag, 1998.

    Google Scholar 

  10. Toivonen H., Klemettinen M., Ronkainen P., Hatonen K., and Mannila H. Pruning and grouping discovered association rules. In Proceedings of the MLnet Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases, Herakhion, Crete, Greece, 1995.

    Google Scholar 

  11. Bayardo Jr. R. J., Agrawal R., and Gunopulos D. Constraint-based rule mining in large, dense databases. Data Mining and Knowledge Discovery, 4(2-3):217-240,2000.

    Article  Google Scholar 

  12. Grabmeier J. and Rudolph A. Techniques of cluster algorithms in data mining. Data Mining and Knowledge Discovery, 6:303-360, 2002.

    Article  MathSciNet  Google Scholar 

  13. Miller R. J. and Yang Y. Association rules over interval data. In Proceedings of the ACM SIGMOD Conference on Management of Data, pages 452-461. ACM SIGMOD, ACM Press, 1997.

    Google Scholar 

  14. Gupta G. K., Strehl A., and Ghosh J. Distance based clustering of association rules. In Proceedings of Intelligent Engineering Systems through Artificial Neural Networks: ANNIE (1999), volume 9, pages 759-764, 1999.

    Google Scholar 

  15. Jain A. K., Murty M. N., and Flynn P. J. Data clustering: A review. ACM Computing Surveys, 31(3):264-323, 1999.

    Article  Google Scholar 

  16. Wang K., Tay S. H. W., and Liu B. Interestingness-based interval merger for numeric association rules. In Proceedings of the International Conference on Data Mining and Knowledge Discovery, pages 121-128, New York City, August 1998. AAAI.

    Google Scholar 

  17. Kaufman L. and Rousseeuw P. J. Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, 1990.

    Google Scholar 

  18. Anderberg M. R. Cluster Analysis for Applications. Academic Press, 1973.

    Google Scholar 

  19. Sahar S. Exploring interestingness through clustering: A framework. In Proceedings of the IEEE International Conference on Data Mining (ICDM 2002), pages 677-680. IEEE, IEEE Computer Society Press, 2002.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Natarajan, R., Shekar, B. (2007). Understandability of Association Rules: A Heuristic Measure to Enhance Rule Quality. In: Guillet, F.J., Hamilton, H.J. (eds) Quality Measures in Data Mining. Studies in Computational Intelligence, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-44918-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-44918-8_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44911-9

  • Online ISBN: 978-3-540-44918-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics