引用本文:王秋萍,丁成,王晓峰.一种基于改进KH与KHM聚类的混合数据聚类算法[J].控制与决策,2020,35(10):2449-2458
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览次   下载 本文二维码信息
码上扫一扫!
分享到: 微信 更多
一种基于改进KH与KHM聚类的混合数据聚类算法
王秋萍,丁成,王晓峰
(西安理工大学理学院,西安710054)
摘要:
为解决K-means聚类对初始聚类中心敏感和易陷入局部最优的问题,提出一种基于改进磷虾群算法与K-harmonic means的混合数据聚类算法.提出一种具有莱维飞行和交叉算子的磷虾群算法以改进磷虾群算法易陷入局部极值和搜索效率低的不足,即在每次标准磷虾群位置更新后加入新的位置更新方法进一步搜索以提高种群的搜索能力,同时交替使用莱维飞行与交叉算子对当前群体位置进行贪婪搜索以增强算法的全局搜索能力.20个标准测试函数的实验结果表明,改进算法不易陷入局部最优解,可在较少的迭代次数下有效地搜索到全局最优解的同时保证算法的稳定性.将改进的磷虾群算法与K调和均值聚类融合,即在每次迭代后用最优个体或经过K调和均值迭代一次后的新个体替换最差个体.5个UCI真实数据集的测试结果表明:融合后的聚类算法能够克服K-means对初始聚类中心敏感的不足且具有较强的全局收敛性.
关键词:  磷虾群算法  莱维飞行  交叉算子  K调和均值聚类  混合聚类
DOI:10.13195/j.kzyjc.2019.0086
分类号:TP301.6
基金项目:国家自然科学基金项目(61772416).
A hybrid data clustering algorithm based on improved krill herd algorithm and KHM clustering
WANG Qiu-ping,DING Cheng,WANG Xiao-feng
(Faculty of Sciences,Xián University of Technology,Xián710054,China)
Abstract:
K-means clustering is sensitive to initial clustering centers and prone to fall into local optimum. In order to solve the problem, a hybrid data clustering algorithm based on an improved krill herd algorithm and K-harmonic means clustering is proposed. Firstly, an improved krill herd algorithm with Lévy flight and crossover operator is proposed to improve stagnating local optimum and low search efficiency of the krill herd algorithm. That is, after each standard krill herd location updating, a new location updating method is added to further search to improve the search ability of the population, at the same time, Lévy flight and crossover operators are used alternately to carry out greedy search for the current population position to enhance the global search ability of the algorithm. The experimental results of 20 benchmark test functions show that the improved algorithm is not easy to fall into the local optimum, which can find the global optimal solution via less times of iteration and ensure the stability of the algorithm. Then, the improved krill herd algorithm and the K-harmonic means clustering algorithm are fused to solve the data clustering problem, that is, the worst individual is replaced by the best individual or the new individual by the K-harmonic means processing the worst individual after each iteration. The test results of five real data sets on UCI show that the fused-clustering algorithm overcomes the defect that K-means is sensitive to the initial clustering center and has stronger global convergence.
Key words:  krill herd algorithm  Lévy flight  crossover operator  K-harmonic means clustering  hybrid clustering

用微信扫一扫

用微信扫一扫