引入改进飞蛾扑火的K均值交叉迭代聚类算法

黄鹤 1; 2; 李昕芮 1; 吴琨 1; 郭璐 3; 王会峰 1; 茹锋 1

doi:10.7652/xjtuxb202009003

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引入改进飞蛾扑火的K均值交叉迭代聚类算法

更新时间：2025-07-09

- 引入改进飞蛾扑火的K均值交叉迭代聚类算法
- Hybrid Iterative K-Means Clustering with Improved Moth-Flame Optimi-ation
- 西安交通大学学报 2020年54卷第9期页码：32-39
- 作者机构：
  
  1. 长安大学电子与控制工程学院,西安,710064
  2. 陕西省道路交通智能检测与装备工程技术研究中心,西安,710064
- 作者简介：
- 基金信息：
  
  : 2019-12-27。作者简介: 黄鹤(1979—),男,副教授;郭璐(通信作者),女,博士,高级工程师。基金项目: 装备预研领域基金资助项目(61403120105);陕西省自然科学基础研究计划资助项目(2019JM-611);陕西省创新人才推进计划青年科技新星项目(2019KJXX-028)
- DOI：10.7652/xjtuxb202009003
  中图分类号： TP301.6
- 网络首发：2020-09-10，
  
  纸质出版：2020
- 稿件说明：
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黄鹤 1, 2, 李昕芮 1, 等. 引入改进飞蛾扑火的K均值交叉迭代聚类算法[J]. 西安交通大学学报, 2020,54(9):32-39.

Hybrid Iterative K-Means Clustering with Improved Moth-Flame Optimi-ation[J]. 2020, 54(9): 32-39.
黄鹤 1, 2, 李昕芮 1, 等. 引入改进飞蛾扑火的K均值交叉迭代聚类算法[J]. 西安交通大学学报, 2020,54(9):32-39. DOI： 10.7652/xjtuxb202009003.

Hybrid Iterative K-Means Clustering with Improved Moth-Flame Optimi-ation[J]. 2020, 54(9): 32-39. DOI： 10.7652/xjtuxb202009003.

摘要

针对现有K均值聚类(KMC)算法在选取初始聚类中心时随机性较大、全局搜索能力差、聚类精度低等问题

提出了一种引入改进飞蛾扑火的K均值交叉迭代聚类(IMFO-KMC)算法。利用最大最小距离积法初始化聚类中心

避免了KMC算法对随机初始聚类中心较为敏感的问题; 利用样条插值预测的思想改进飞蛾扑火算法

提高了算法的收敛速度及寻优精度; 以类内平均距离为适应度函数

引导插值扑火算法优化KMC迭代过程中的聚类中心

提高了聚类精度。将IMFO-KMC与KMC、K-means++算法、模糊c均值聚类算法在国际标准数据集Iris、Wine和Seeds上进行了实验对比

结果表明:IMFO-KMC算法在Iris数据集上的性能提升最为明显

相比其他算法准确率提高了0.67%～4.18%

标准化互信息提高了1.5%～4.01%。

Abstract

A new K-means clustering algorithm with improved moth-flame optimi-ation is proposed to solve the problem that the current K-means clustering(KMC)algorithm has great randomness in selecting the initial clustering center

poor global search ability and low clustering accuracy. The maximum and minimum distance function is adopted to initiali-e the clustering center to avoid the problem that KMC algorithm is sensitive to the random initial clustering center. Then the moth-flame optimi-ation is improved by spline interpolation to heighten the convergence rate and optimi-ation accuracy. The average distance category is taken as the fitness function to guide interpolation moth-flame optimi-ation to optimi-e the clustering center in the process of KMC iteration so as to improve the clustering accuracy. Compared with KMC algorithm

K-means++ algorithm and fu--y c-means clustering algorithm on the international standard data sets Iris

Wine and Seeds

the experimental results show that the IMFO

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