Journal of Frontiers of Computer Science and Technology ›› 2021, Vol. 15 ›› Issue (5): 777-793.DOI: 10.3778/j.issn.1673-9418.2010048

• Surveys and Frontiers • Previous Articles     Next Articles

Survey of Particle Swarm Optimization Algorithm for Association Rule Mining

ZHONG Qianyi, QIAN Qian, FU Yunfa, FENG Yong   

  1. Yunnan Key Laboratory of Computer Technology Applications, School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China
  • Online:2021-05-01 Published:2021-04-30



  1. 昆明理工大学 信息工程与自动化学院 云南省计算机技术应用重点实验室,昆明 650500


Association rule mining is an important area in data mining, considering the large scale, high dimen-sionality, modal diversity and type complexity of current data, traditional association rule mining algorithms cannot meet the needs of big data. The particle swarm optimization algorithm, as an efficient intelligent algorithm, provides a new solution and has been widely used in association rule mining field in recent years. This paper introduces the basic principle of swarm optimization algorithm and the basic concept of association rules, and reviews the research progress of the swarm optimization algorithm itself. Then, this paper further summarizes the researches of the swarm optimization algorithm in association rule mining problem, including common data conversion methods, coding methods, and evaluation indexes. These improved algorithms from related researches are compared with other algorithms widely used in association rule mining, and their advantages, disadvantages, and application scenarios are discussed. After that, the existing improvement algorithms are systematically classified according to its methods, such as parameter, variation, and hybrid algorithm improvements, and the application areas of particle swarm optimization algorithms in association rule mining are also summarized, such as shopping baskets, finances, medical, industrial productions and risk assessments. At last, based on the introduction of the latest research pro-gress in this field, further research directions are discussed by analyzing the existing problems.

Key words: association rule mining, particle swarm optimization algorithm, intelligent algorithm


关联规则挖掘是数据挖掘中的重要领域,考虑到当前数据的大规模、高维度、模态多样及类型复杂等特性,传统关联规则挖掘算法已无法适应大数据的需求,粒子群优化算法作为一种高效的智能优化算法,为其提供了一种全新的解决方案,近年来被广泛应用于该领域。首先对粒子群优化算法的基本原理及关联规则的基本概念进行了详细介绍,回顾了粒子群优化算法的研究进展,分析了粒子群优化算法在关联规则挖掘中的研究, 包括常用的数据转换方法、编码方式及评估指标,并与其他在关联规则挖掘中被广泛应用的算法进行了对比,总结了各自的优缺点及适用场景。然后对已有改进方法进行了较为系统的分类,即分为基于参数、基于变异机制和混合其他算法的改进。接着梳理归纳了粒子群优化算法在关联规则挖掘中的应用领域,阐述了该算法在购物篮、金融、医疗、工业生产及风险评估领域中的应用优势。最后在介绍这一领域的最新研究进展的基础上,通过对现存问题进行分析,讨论了进一步的研究方向。

关键词: 关联规则挖掘, 粒子群优化算法, 智能算法