A survey of evolutionary computation for association rule mining

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@Article{TELIKANI2020,

• author = "Akbar Telikani and Amir H. Gandomi and Asadollah Shahbahrami",
• title = "A survey of evolutionary computation for association rule mining",
• journal = "Information Sciences",
• year = "2020",
• volume = "524",
• pages = "318--352",
• month = jul,
• keywords = "genetic algorithms, genetic programming, Data mining, Association rule mining, Evolutionary computation, Swarm intelligent",
• ISSN = "0020-0255",
• URL = "http://www.sciencedirect.com/science/article/pii/S002002552030164X",
• DOI = "doi:10.1016/j.ins.2020.02.073",
• abstract = "Association Rule Mining (ARM) is a significant task for discovering frequent patterns in data mining. It has achieved great success in a plethora of applications such as market basket, computer networks, recommendation systems, and healthcare. In the past few years, evolutionary computation-based ARM has emerged as one of the most popular research areas for addressing the high computation time of traditional ARM. Although numerous papers have been published, there is no comprehensive analysis of existing evolutionary ARM methodologies. we review emerging research of evolutionary computation for ARM. We discuss the applications on evolutionary computations for different types of ARM approaches including numerical rules, fuzzy rules, high-utility itemsets, class association rules, and rare association rules. Evolutionary ARM algorithms were classified into four main groups in terms of the evolutionary approach, including evolution-based, swarm intelligence-based, physics-inspired, and hybrid approaches. Furthermore, we discuss the remaining challenges of evolutionary ARM and discuss its applications and future topics.",
• notes = "Also known as \cite{TELIKANI2020318} Highlights

  We present a review of trends and directions in EC-based association rule mining.

  221 algorithms were collected between 2000 and 2019 using a research methodology.

  We review algorithms according to meta-heuristic approaches in nine groups.

  Applications and the current problems and opportunities are described.",

}

Genetic Programming entries for Akbar Telikani A H Gandomi Asadollah Shahbahrami

Citations