[1]黄鹤,李昕芮,吴琨,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(09):032-39.[doi:10.7652/xjtuxb202009003]
 HUANG He,LI Xinrui,WU Kun,et al.Hybrid Iterative K-Means Clustering with Improved Moth-Flame Optimization[J].Journal of Xi'an Jiaotong University,2020,54(09):032-39.[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
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《西安交通大学学报》[ISSN:0253-987X/CN:61-1069/T]

卷:
54
期数:
2020年第09期
页码:
032-39
栏目:
出版日期:
2020-09-10

文章信息/Info

Title:
Hybrid Iterative K-Means Clustering with Improved Moth-Flame Optimization
文章编号:
0253-987X(2020)09-0032-08
作者:
黄鹤12 李昕芮1 吴琨1 郭璐3 王会峰1 茹锋1
1.长安大学电子与控制工程学院, 710064, 西安; 2.陕西省道路交通智能检测与装备工程技术研究中心, 710064, 西安; 3.西北工业大学无人机系统国家工程研究中心, 710072, 西安
Author(s):
HUANG He12 LI Xinrui1 WU Kun1 GUO Lu3 WANG Huifeng1 RU Feng1
1. School of Electronic Control, Chang’an University, Xi’an 710064, China; 2. Shaanxi Road Traffic Intelligent Detection and Equipment Engineering Technology Research Center, Xi’an 710064, China; 3. UAV National Engineering Research Center, Northwestern Polytechnical University, Xi’an 710072, China
关键词:
飞蛾扑火算法 聚类中心 K均值聚类 类内平均距离 最大最小距离积法
Keywords:
moth-flame optimization cluster center K-means clustering average distance category maximum and minimum distance product
分类号:
TP301.6
DOI:
10.7652/xjtuxb202009003
文献标志码:
A
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 optimization 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 initialize the clustering center to avoid the problem that KMC algorithm is sensitive to the random initial clustering center. Then the moth-flame optimization is improved by spline interpolation to heighten the convergence rate and optimization accuracy. The average distance category is taken as the fitness function to guide interpolation moth-flame optimization to optimize the clustering center in the process of KMC iteration so as to improve the clustering accuracy. Compared with KMC algorithm, K-means++ algorithm and fuzzy c-means clustering algorithm on the international standard data sets Iris, Wine and Seeds, the experimental results show that the IMFO-KMC algorithm achieves the most significant improvement on the Iris data set. Compared with the other algorithms, the accuracy of IMFO-KMC algorithm is improved by 0.67% - 4.18%, and the normalized mutual information is improved by 1.5% - 4.01%.

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备注/Memo

备注/Memo:
收稿日期: 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)。
更新日期/Last Update: 2020-09-10