A Systematic Assessment of Numerical Association Rule Mining Methods

被引:0
作者
Kaushik M. [1 ]
Sharma R. [1 ]
Peious S.A. [1 ]
Shahin M. [1 ]
Yahia S.B. [2 ]
Draheim D. [1 ]
机构
[1] Information Systems Group, Tallinn University of Technology, Akadeemia tee 15a, Tallinn
[2] Software Science Department, Tallinn University of Technology, Akadeemia tee 15a, Tallinn
关键词
Association rule mining; Data mining; Knowledge discovery in databases; Numerical association rule mining; Quantitative association rule mining;
D O I
10.1007/s42979-021-00725-2
中图分类号
学科分类号
摘要
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. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
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