Fundamentals of association rules in data mining and knowledge discovery

Authors

  • Shichao Zhang,

    Corresponding author
    1. Department of Computer Science, Zhejiang Normal University, Jinhua, Zhejiang, PR China
    2. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, Jiangsu, PR China
    • Department of Computer Science, Zhejiang Normal University, Jinhua, Zhejiang, PR China
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  • Xindong Wu

    1. Department of Computer Science, University of Vermont, Burlington, VT, USA
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Abstract

Association rule mining is one of the fundamental research topics in data mining and knowledge discovery that identifies interesting relationships between itemsets in datasets and predicts the associative and correlative behaviors for new data. Rooted in market basket analysis, there are a great number of techniques developed for association rule mining. They include frequent pattern discovery, interestingness, complex associations, and multiple data source mining. This paper introduces the up-to-date prevailing association rule mining methods and advocates the mining of complete association rules, including both positive and negative association rules. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 97-116 DOI: 10.1002/widm.10

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