Encyclopedia of Database Systems

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

Association Rule Mining on Streams

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


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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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