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A Systematic Assessment of Numerical Association Rule Mining Methods

Abstract

In data mining, the classical association rule mining techniques deal with binary attributes; however, real-world data have a variety of attributes (numerical, categorical, Boolean). To deal with the variety of data attributes, the classical association rule mining technique was extended to numerical association rule mining. Initially, the concept of numerical association rule mining started with the discretization method, and later, many other methods, e.g., optimization, distribution are proposed in state-of-the-art. Different authors have presented various algorithms for each numerical association rule mining method; therefore, it is hard to select a suitable algorithm for a numerical association rule mining task. In this article, we present a systematic assessment of various numerical association rule mining methods and we provide a meta-study of thirty numerical association rule mining algorithms. We investigate how far the discretization techniques have been used in the numerical association rule mining methods.

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Correspondence to Minakshi Kaushik.

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This work has been partially conducted in the project “ICT programme” which was supported by the European Union through the European Social Fund.

This article is part of the topical collection “Future Data and Security Engineering 2020” guest edited by Tran Khanh Dang.

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Kaushik, M., Sharma, R., Peious , S.A. et al. A Systematic Assessment of Numerical Association Rule Mining Methods. SN COMPUT. SCI. 2, 348 (2021). https://doi.org/10.1007/s42979-021-00725-2

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  • DOI: https://doi.org/10.1007/s42979-021-00725-2

Keywords

  • Knowledge discovery in databases
  • Data mining
  • Association rule mining
  • Numerical association rule mining
  • Quantitative association rule mining