ck-FARM: An R package to discover big data associations for business intelligence

George To Sum Ho; Yung Po Tsang; Qin Wu; Valerie Tang

doi:10.1016/j.softx.2023.101341

ck-FARM: An R package to discover big data associations for business intelligence

George To Sum Ho, Yung Po Tsang, Qin Wu, Valerie Tang

Research output: Journal article publication › Journal article › Academic research › peer-review

2 Citations (Scopus)

Abstract

Fuzzy association rule mining (FARM) is a well-known data mining algorithm to identify frequently occurring patterns from datasets, in which the fuzzy set theory is applied to consider linguistic variables for building an explainable reasoning system. In this study, an improved algorithm, namely ck-FARM, is proposed as an R package. Differing from the typical FARM mechanism, the statistical significance of the parameter correspondence is examined, while the fuzzy membership functions are autonomously built and adapted to the given datasets. Consequently, the adaptability and reliability of building fuzzy association rules can be enhanced.

Original language	English
Article number	101341
Number of pages	5
Journal	SoftwareX
Volume	22
DOIs	https://doi.org/10.1016/j.softx.2023.101341
Publication status	Published - May 2023

Keywords

Association rule mining
Big data
Business intelligence
R package

ASJC Scopus subject areas

Software
Computer Science Applications

More information

10.1016/j.softx.2023.101341

Cite this

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title = "ck-FARM: An R package to discover big data associations for business intelligence",

abstract = "Fuzzy association rule mining (FARM) is a well-known data mining algorithm to identify frequently occurring patterns from datasets, in which the fuzzy set theory is applied to consider linguistic variables for building an explainable reasoning system. In this study, an improved algorithm, namely ck-FARM, is proposed as an R package. Differing from the typical FARM mechanism, the statistical significance of the parameter correspondence is examined, while the fuzzy membership functions are autonomously built and adapted to the given datasets. Consequently, the adaptability and reliability of building fuzzy association rules can be enhanced.",

keywords = "Association rule mining, Big data, Business intelligence, R package",

author = "Ho, {George To Sum} and Tsang, {Yung Po} and Qin Wu and Valerie Tang",

note = "The authors would like to acknowledge the support by the University Grants Committee of Hong Kong (RMGS Project No.: 700007), and the Department of Supply Chain and Information Management of The Hang Seng University of Hong Kong, ITC and Unlimic Limited (ITF Project No.: PRP/034/20FX). In addition, the work described in this paper was partially supported by a grant from The Hong Kong Polytechnic University (Project ID: P0038722). Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = may,

doi = "10.1016/j.softx.2023.101341",

language = "English",

volume = "22",

journal = "SoftwareX",

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AU - Ho, George To Sum

AU - Tsang, Yung Po

AU - Wu, Qin

AU - Tang, Valerie

N1 - The authors would like to acknowledge the support by the University Grants Committee of Hong Kong (RMGS Project No.: 700007), and the Department of Supply Chain and Information Management of The Hang Seng University of Hong Kong, ITC and Unlimic Limited (ITF Project No.: PRP/034/20FX). In addition, the work described in this paper was partially supported by a grant from The Hong Kong Polytechnic University (Project ID: P0038722). Publisher Copyright: © 2023 The Author(s)

PY - 2023/5

Y1 - 2023/5

N2 - Fuzzy association rule mining (FARM) is a well-known data mining algorithm to identify frequently occurring patterns from datasets, in which the fuzzy set theory is applied to consider linguistic variables for building an explainable reasoning system. In this study, an improved algorithm, namely ck-FARM, is proposed as an R package. Differing from the typical FARM mechanism, the statistical significance of the parameter correspondence is examined, while the fuzzy membership functions are autonomously built and adapted to the given datasets. Consequently, the adaptability and reliability of building fuzzy association rules can be enhanced.

AB - Fuzzy association rule mining (FARM) is a well-known data mining algorithm to identify frequently occurring patterns from datasets, in which the fuzzy set theory is applied to consider linguistic variables for building an explainable reasoning system. In this study, an improved algorithm, namely ck-FARM, is proposed as an R package. Differing from the typical FARM mechanism, the statistical significance of the parameter correspondence is examined, while the fuzzy membership functions are autonomously built and adapted to the given datasets. Consequently, the adaptability and reliability of building fuzzy association rules can be enhanced.

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KW - Big data

KW - Business intelligence

KW - R package

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DO - 10.1016/j.softx.2023.101341

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ER -

ck-FARM: An R package to discover big data associations for business intelligence

Abstract

Keywords

ASJC Scopus subject areas

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