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Approximation of Frequent Itemsets

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Encyclopedia of Database Systems

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Recommended Reading

  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.

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  6. Liu J, Paulsen S, Sun X, Wang W, Nobel A, Prins J. Mining Approximate frequent itemset in the presence of noise: algorithm and analysis. In: Proceedings of the SIAM International Conference on Data Mining; 2006. p. 405–11.

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  7. Pei J, Tung AK, Han J. Fault-tolerant frequent pattern mining: problems and challenges. In: Proceedings of the Workshop on Research Issues in Data Mining and Knowledge Discovery; 2001.

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  8. UCI machine learning repository. http://www.ics.uci.edu/mlearn/MLSummary.html

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Author information

Authors and Affiliations

  1. University of Kentucky, Lexington, KY, USA

    Jinze Liu

Authors
  1. Jinze Liu
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Corresponding author

Correspondence to Jinze Liu .

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

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Liu, J. (2018). Approximation of Frequent Itemsets. 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_22

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