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Encyclopedia of Database Systems pp 177–181Cite as

Association Rule Mining on Streams

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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 X ⊆ T. 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 X ⇒ Y holds if X ∪ Y 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

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Author information

Authors and Affiliations

  1. Computer Science Department, University of Illinois at Chicago, Chicago, IL, USA

    Philip S. Yu

  2. NEC Laboratories America, Cupertino, CA, USA

    Yun Chi

Authors
  1. Philip S. Yu
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  2. Yun Chi
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Corresponding author

Correspondence to Philip S. Yu .

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. AT&T Labs-Research, Bedminster, NJ, USA

    Divesh Srivastava

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Yu, P.S., Chi, Y. (2018). Association Rule Mining on Streams. 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_25

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