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山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (4): 74-82.doi: 10.6040/j.issn.1672-3961.0.2022.268

• 机器学习与数据挖掘 • 上一篇    

变异萤火虫优化的粗糙K-均值聚类算法

李兆彬1,叶军1,2,周浩岩1,卢岚1,谢立1   

  1. 1. 南昌工程学院信息工程学院, 江西 南昌 330000;2. 江西省水信息协同感知与智能处理重点实验室(南昌工程学院), 江西 南昌 330000
  • 发布日期:2023-08-18
  • 作者简介:李兆彬(1998— ),男,河南安阳人,硕士研究生,主要研究方向为粗糙集与粒计算理论、聚类算法、群智能算法. E-mail:1012782202@qq.com
  • 基金资助:
    国家自然科学基金资助项目(61562061);江西省教育厅科技项目(GJJ211920、GJJ170995)

A rough K-means clustering algorithm optimized by mutation firefly algorithm

LI Zhaobin1, YE Jun1,2, ZHOU Haoyan1, LU Lan1, XIE Li1   

  1. 1. College of Information Engineering, Nanchang Institute of Engineering, Nanchang 330000, Jiangxi, China;
    2. Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing(Nanchang Institute of Engineering), Nanchang 330000, Jiangxi, China
  • Published:2023-08-18

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摘要： 粗糙K-means聚类算法存在如下不足:随机选取初始聚类中心会导致算法过早收敛容易陷入局部最优,质心更新公式中的权重和决定对象划分的阈值采用定值有时会导致聚类结果波动较大和精度下降。针对以上问题,引入一种变异策略和差分进化的萤火虫算法,从3个方面进行优化:构造新的目标函数,以目标函数值作为萤火虫光亮强度进行初始聚类中心点的搜索,把萤火虫算法求得的最优解作为算法的聚类中心进行聚类迭代;以下近似集和边界集中对象数量的变化以及对象分布的差异性动态调整质心权重;给出一种通过迭代次数自动获取阈值的方法。试验结果表明,改进后的算法减少了迭代次数,聚类结果稳定性好,准确率更高,改善了算法对随机初始中心点的敏感和稳定性不足等问题。

关键词: K-means聚类算法, 萤火虫算法, 变异, 粗糙集

中图分类号: 

  • TP391
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