Encyclopedia of Database Systems

2018 Edition
| Editors: Ling Liu, M. Tamer Özsu

Association Rule Mining on Streams

  • Philip S. Yu
  • Yun Chi
Reference work entry
DOI: https://doi.org/10.1007/978-1-4614-8265-9_25

Definition

Let I = {i1, …, im} be a set of items. Let S be a stream of transactions in a sequential order where each transaction is a subset of I. For an itemset X, which is a subset of I, a transaction T in S is said to contain the itemset X if XT. The support of X is defined as the fraction of transactions in S that contain X. For a given support threshold s%, X is frequent if the support of X is greater than or equal to s%, i.e., if at least s% transactions in S contain X. For a given confidence threshold c%, an association rule XY holds if XY is frequent and at least c% of transactions in S that contain X also contain Y. The problem of association rule mining on streams is to discover all association rules that hold in a stream of transactions.

Historical Background

In 1993, Rakesh Agrawal et al. [1] proposed the framework for association rule mining. Since this seminal work, a lot of research work has been done to improve the efficiency of association rule mining...

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Recommended Reading

  1. 1.
    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
  2. 2.
    Chang JH, Lee WS. Finding recent frequent itemsets adaptively over online data streams. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2003. p. 487–92.Google Scholar
  3. 3.
    Charikar M, Chen K, Farach-Colton M. Finding frequent items in data streams. In: Proceedings of the 29th International Colloquium on Automata, Languages and Programming; 2002. p. 693–703.CrossRefGoogle Scholar
  4. 4.
    Cheng J, Ke Y, Ng W. A survey on algorithms for mining frequent itemsets over data streams. Knowl Int Syst. 2008;16(1):1–27.CrossRefGoogle Scholar
  5. 5.
    Chi Y, Wang H, Yu PS, Muntz RR. Catch the moment: maintaining closed frequent itemsets in a data stream sliding window. Knowl Inf Syst. 2006;10(3):265–94.CrossRefGoogle Scholar
  6. 6.
    Cheung DW, Han J, Ng V, Wong CY. Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the 12th International Conference on Data Engineering; 1996. p. 106–14.Google Scholar
  7. 7.
    Cheung DW, Lee SD, Kao B. A general incremental technique for maintaining discovered association rules. In: Proceedings of the 5th Interenational Conference on Database Systems for Advanced Applications; 1997. p. 185–94.Google Scholar
  8. 8.
    Giannella C, Han J, Pei J, Yan X, Yu PS. Mining frequent patterns in data streams at multiple time granularities. In: Kargupta H, Joshi A, Sivakumar K, Yesha Y, editors. Data mining: next generation challenges and future directions. AAAI; 2004.Google Scholar
  9. 9.
    Gouda K, Zaki MJ. Efficiently mining maximal frequent itemsets. In: Proceedings of the 1st IEEE Interenational Conference on Data Mining; 2001. p. 163–70.Google Scholar
  10. 10.
    Manku G, Motwani R. Approximate frequency counts over data streams. In: Proceedings of the 28th International Conference on Very Large Data Bases; 2002. p. 346–57.CrossRefGoogle Scholar
  11. 11.
    Otey ME, Parthasarathy S, Wang C, Veloso A, Meira W Jr. Parallel and distributed methods for incremental frequent itemset mining. IEEE Trans Syst Man Cybern B. 2004;34(6):2439–50.CrossRefGoogle Scholar
  12. 12.
    Teng W-G, Chen M-S, Yu PS. A regression-based temporal pattern mining scheme for data streams. In: Proceedings of the 29th International Conference on Very Large Data Bases; 2003. p. 98–104.CrossRefGoogle Scholar
  13. 13.
    Thomas S, Bodagala S, Alsabti K, Ranka S. An efficient algorithm for the incremental updation of association rules in large databases. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining; 1997. p. 263–6.Google Scholar
  14. 14.
    Veloso A, Meira Jr W, de Carvalho M, Pôssas B, Parthasarathy S, Zaki MJ. Mining frequent itemsets in evolving databases. In: Proceedings of the SIAM International Conference on Data Mining; 2002.Google Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of Illinois at ChicagoChicagoUSA
  2. 2.NEC Laboratories AmericaCupertinoUSA

Section editors and affiliations

  • Divesh Srivastava
    • 1
  1. 1.AT&T Labs-ResearchBedminsterUSA