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Classification by Association Rule Analysis

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Encyclopedia of Database Systems

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Synonyms

Associative classification

Definition

Given a training dataset D, build a classifier (or a classification model) from D using an association rule mining algorithm. The model can be used to classify future or test cases.

Historical Background

In the previous section, it was shown that a list of rules can be induced or mined from the data for classification. A decision tree may also be converted to a set of rules. It is thus only natural to expect that association rules [1] be used for classification as well. Yes, indeed! Since the first classification system (called CBA) that used association rules was reported in [10], many techniques and systems have been proposed by researchers [2–4, 6–8, 13, 15, 16]. CBA is based on class association rules (CAR), which are a special type of association rules with only a class label on the right-hand-side of each rule. Thus, syntactically or semantically there is no difference between a rule generated by a class association rule miner and a...

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Recommended Reading

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Author information

Authors and Affiliations

  1. University of Illinois at Chicago, Chicago, IL, USA

    Bing Liu

Authors
  1. Bing Liu
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Corresponding author

Correspondence to Bing Liu .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. School of Elec. Eng. and Computer Science, Seoul National Univ., Kwanak P.O. Box 34, 151-742, Seoul, Republic of Korea

    Kyuseok Shim

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© 2016 Springer Science+Business Media New York

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Liu, B. (2016). Classification by Association Rule Analysis. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_558-2

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_558-2

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

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