Skip to main content

Frequent Itemset Mining with Constraints

  • Reference work entry
Encyclopedia of Database Systems

Synonyms

Constrained frequent itemset mining; Frequent pattern mining with constraints; Frequent set mining with constraints

Definition

Let Item ={ item 1,item 2,...,item m } be a set of domain items, where each item represents an object in a specific domain. Each object is associated with some attributes or auxiliary information about the object. A transaction t i =〈tID, I i 〉 is a tuple, where tID is a unique identifier and I i ⊆Item is a set of items. A set of items is also known as an itemset. A transaction database TDB is a collection of transactions. An itemset S is contained in a transaction t i =〈tID, I i 〉 if S ⊆ I i . The support (or frequency) of an itemset S in a database TDB is the number (or percentage) of transactions in TDB containing S. An itemset is frequent if its support exceeds or equals a user-specified support threshold minsup. A user-specified constraint C is a predicate on the powerset of Item (i.e., C: 2Item↦ {true, false}). An itemset S satisfiesa...

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 2,500.00
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

Recommended Reading

  1. Agrawal R., Imielinski T., and Swami A. Mining association rules between sets of items in large databases. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2003, pp. 207–216.

    Google Scholar 

  2. Agrawal R. and Srikant R. Fast algorithms for mining association rules. In Proc. 20th Int. Conf. on Very Large Data Bases, 1994, pp. 487–499.

    Google Scholar 

  3. Bonchi F. and Lucchese C. Pushing tougher constraints in frequent pattern mining. In Advances in Knowledge Discovery and Data Mining, 9th Pacific-Asia Conf., 2005, pp. 114–124.

    Google Scholar 

  4. Bucila C., Gehrke J., Kifer D., and White W. DualMiner: a dual-pruning algorithm for itemsets with constraints. In Proc. 8th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2002, pp. 42–51.

    Google Scholar 

  5. Gade K., Wang J., and Karypis G. Efficient closed pattern mining in the presence of tough block constraints. In Proc. 10th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2004, pp. 138–147.

    Google Scholar 

  6. Grahne G., Lakshmanan L.V.S., and Wang X. Efficient mining of constrained correlated sets. In Proc. 16th Int. Conf. on Data Engineering, 2000, pp. 512–521.

    Google Scholar 

  7. Han J., Pei J., and Yin Y. Mining frequent patterns without candidate generation. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2000, pp. 1–12.

    Google Scholar 

  8. Lakshmanan L.V.S., Leung C.K.-S., and Ng R.T. Efficient dynamic mining of constrained frequent sets. ACM Trans. Database Syst., 28(4):337–389, 2003.

    Article  Google Scholar 

  9. Lakshmanan L.V.S., Ng R., Han J., and Pang A. Optimization of constrained frequent set queries with 2-variable constraints. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1999, pp. 157–168.

    Google Scholar 

  10. Leung C.K.-S., Lakshmanan L.V.S., and Ng R.T. Exploiting succinct constraints using FP-trees. ACM SIGKDD Explor., 4(1):40–49, 2002.

    Article  Google Scholar 

  11. Ng R.T., Lakshmanan L.V.S., Han J., and Pang A. Exploratory mining and pruning optimizations of constrained associations rules. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 1998, pp. 13–24.

    Google Scholar 

  12. Pei J. and Han J. Can we push more constraints into frequent pattern mining? In Proc. 6th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 2000, pp. 350–354.

    Google Scholar 

  13. Pei J., Han J., and Lakshmanan L.V.S. Mining frequent item sets with convertible constraints. In Proc. 17th Int. Conf. on Data Engineering, 2001, pp. 433–442.

    Google Scholar 

  14. Srikant R., Vu Q., and Agrawal R. Mining association rules with item constraints. In Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining, 1997, pp. 67–73.

    Google Scholar 

  15. Yun U. and Leggett J. WLPMiner: weighted frequent pattern mining with length-decreasing support constraints. In Advances in Knowledge Discovery and Data Mining, 9th Pacific-Asia Conf., 2005, pp. 555–567.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer Science+Business Media, LLC

About this entry

Cite this entry

Leung, C.KS. (2009). Frequent Itemset Mining with Constraints. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_170

Download citation

Publish with us

Policies and ethics