BPSO Algorithm with Opposition-Based Learning Method for Association Rule Mining

Qian, Qian; Feng, Yong; Fu, Yunfa

doi:10.1007/978-981-16-1843-7_41

Cited by 1 publication

(2 citation statements)

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“…Among them, the PSO algorithm has already received the attention of many scholars at home and abroad since it was proposed because of its few adjustable parameters, high efficiency, easy implementation, and fast convergence speed. The current research shows that ARM using the PSO algorithms has higher operational efficiency compared with other data mining algorithms and can generate more accurate and valuable rules [13][14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 91%

“…The ant colony optimization (ACO) algorithm uses pheromone to decide the search direction, which consumes a lot of time for a solution construction and also has the problem of a long convergence time and is prone to stagnation in high-dimensional data. The particle swarm optimization (PSO) algorithm is efficient and simple to implement, converges quickly, and can adjust the search direction by iteration, which is significantly better than other heuristics in ARM problems [92]. PSO algorithms are widely used in ARM, mainly in the fields of privacy protection, recommender systems, risk assessment, and medicine.…”

Section: Applicationmentioning

confidence: 99%

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A Survey on Particle Swarm Optimization for Association Rule Mining

Wang

Chen

et al. 2022

Electronics

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Association rule mining (ARM) is one of the core techniques of data mining to discover potentially valuable association relationships from mixed datasets. In the current research, various heuristic algorithms have been introduced into ARM to address the high computation time of traditional ARM. Although a more detailed review of the heuristic algorithms based on ARM is available, this paper differs from the existing reviews in that we expected it to provide a more comprehensive and multi-faceted survey of emerging research, which could provide a reference for researchers in the field to help them understand the state-of-the-art PSO-based ARM algorithms. In this paper, we review the existing research results. Heuristic algorithms for ARM were divided into three main groups, including biologically inspired, physically inspired, and other algorithms. Additionally, different types of ARM and their evaluation metrics are described in this paper, and the current status of the improvement in PSO algorithms is discussed in stages, including swarm initialization, algorithm parameter optimization, optimal particle update, and velocity and position updates. Furthermore, we discuss the applications of PSO-based ARM algorithms and propose further research directions by exploring the existing problems.

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