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.

中文翻译：

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关联规则挖掘的粒子群优化综述

关联规则挖掘 (ARM) 是数据挖掘的核心技术之一，用于从混合数据集中发现潜在有价值的关联关系。在目前的研究中，各种启发式算法已经被引入到 ARM 中，以解决传统 ARM 计算时间长的问题。尽管有更详细的基于 ARM 的启发式算法的综述，但本文与现有综述的不同之处在于，我们希望它能够对新兴研究进行更全面和多方面的调查，从而为研究领域的研究人员提供参考。帮助他们了解最先进的基于 PSO 的 ARM 算法。在本文中，我们回顾了现有的研究成果。ARM 的启发式算法分为三个主要组，包括生物启发、物理启发和其他算法。此外，本文还描述了不同类型的ARM及其评估指标，并分阶段讨论了PSO算法的改进现状，包括群体初始化、算法参数优化、最优粒子更新以及速度和位置更新。此外，我们讨论了基于 PSO 的 ARM 算法的应用，并通过探索存在的问题提出了进一步的研究方向。