Skip to main content
SpringerLink
Log in

Frequent Itemsets and Association Rules

  • Reference work entry
  • First Online:
Book cover Encyclopedia of Database Systems

  • 28 Accesses

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 4,499.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 6,499.99
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

Institutional subscriptions

Recommended Reading

  1. Agrawal R, Gehrke J, Gunopulos D, Raghavan P. Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1998. p. 94–105.

    Google Scholar 

  2. Agrawal R, Imielinski T, Swami A. Mining association rules between sets of items in large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1993. p. 207–16.

    Google Scholar 

  3. Agrawal R, Srikant R. Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases; 1994. p. 487–99.

    Google Scholar 

  4. Brin S, Motwani R, Ullman JD, Tsur S. Dynamic itemset counting and implication rules for market basket analysis. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1997. p. 255–64.

    Google Scholar 

  5. Cheng C-H, Fu AW, Zhang Y. Entropy-based subspace clustering for mining numerical data. In: Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 1999. p. 84–93.

    Google Scholar 

  6. Cheng H, Yan X, Han J, Hsu C. Discriminative frequent pattern analysis for effective classification. In: Proceedings of the 23rd International Conference on Data Engineering; 2007. p. 716–25.

    Google Scholar 

  7. Cong G., Tan K-L, Tung AKH, Xu X. Mining top-k covering rule groups for gene expression data. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2005. p. 670–81.

    Google Scholar 

  8. Eirinaki M, Vazirgiannis M. Web mining for web personalization. ACM Trans Internet Technol. 2003;3(1):1–27.

    Article  Google Scholar 

  9. Goethals B, Zaki M. An introduction to workshop on frequent itemset mining implementations. In: Proceedings of the ICDM International Workshop on Frequent Itemset Mining Implementations; 2003.p. 1–13.

    Google Scholar 

  10. Han J, Pei J, Yin Y. Mining frequent patterns without candidate generation. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2000. p. 1–12.

    Article  Google Scholar 

  11. Li W, Han J, Pei J. CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of the 1st IEEE International Conference on Data Mining; 2001. p. 369-376.

    Google Scholar 

  12. Li Z, Zhou Y. PR-Miner: automatically extracting implicit programming rules and detecting violations in large software code. In: Proceedings of the ACM SIGSOFT Symposium on Foundations Software Engineering; 2005. p. 306–15.

    Article  Google Scholar 

  13. Liu B., Hsu W, Ma Y. Integrating classification and association rule mining. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining; 1998. p. 80–6.

    Google Scholar 

  14. Park JS, Chen MS, Yu PS. An effective hash-based algorithm for mining association rules. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 1995. p. 175–86.

    Google Scholar 

  15. Pei J, Han J, Mortazavi B-A, Zhu H. Mining access patterns efficiently from web logs. In: Advances in Knowledge Discovery and Data Mining, 4th Pacific-Asia Conference; 2000. p. 396–407.

    Google Scholar 

  16. Savasere A., Omiecinski E., Navathe S. An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21th International Conference on Very Large Data Bases; 1995. p. 432–43.

    Google Scholar 

  17. Srivastava J, Cooley R, Deshpande M, Tan P. Web usage mining: discovery and applications of usage patterns from web data. SIGKDD Explor. 2000;2(1):12–23.

    Article  Google Scholar 

  18. Toivonen H. Sampling large databases for association rules. In: Proceedings of the 22nd International Conference on Very Large Data Bases; 1996. p. 134–45.

    Google Scholar 

  19. Wang H, Wang W, Yang J, Yu PS. Clustering by pattern similarity in large data sets. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2002. p. 418–27.

    Google Scholar 

  20. Zaki MJ. Scalable algorithms for association mining. IEEE Trans Knowl Data Eng. 2000;12(3):372–90.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Hong Kong, China

    Hong Cheng

  2. University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Jiawei Han

Authors
  1. Hong Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiawei Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hong Cheng .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, GA, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, ON, Canada

    M. Tamer Özsu

Section Editor information

  1. School of Computing Science, Simon Fraser Univ., Burnaby, British Columbia, Canada

    Jian Pei

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Science+Business Media, LLC, part of Springer Nature

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Cheng, H., Han, J. (2018). Frequent Itemsets and Association Rules. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_171

Download citation

Publish with us

Policies and ethics

Search

Navigation