基于K-means聚类算法优化方法的研究
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摘要
针对传统K-means聚类中存在的一系列问题,文中提出了一种基于K-means聚类的改进算法。该算法首先利用K-means++聚类从数据中选择K个距离尽可能远的对象作为初始聚类中心,然后利用K-mediods聚类选择数据样本的中位数作为聚类中心的对象,最后与两步聚类结合。通过对几个常用UCI标准数据集进行仿真实验,结果表明该算法比传统算法更优。
Aiming at a series of problems in traditional K-means clustering,this paper proposes an improved algorithm based on K-means clustering. The algorithm uses K-means + + clustering to select K objects as far as possible from the data as the initial clustering center firstly,and then uses K-mediods clustering to select the median of the data samples as the cluster center object,and finally combined with Two-step clustering. Simulation experiments on several common UCI standard datasets show that the proposed algorithm is superior to traditional algorithms.
Aiming at a series of problems in traditional K-means clustering,this paper proposes an improved algorithm based on K-means clustering. The algorithm uses K-means + + clustering to select K objects as far as possible from the data as the initial clustering center firstly,and then uses K-mediods clustering to select the median of the data samples as the cluster center object,and finally combined with Two-step clustering. Simulation experiments on several common UCI standard datasets show that the proposed algorithm is superior to traditional algorithms.
引文
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[8]Lu W J,Yan Z Z. Improved FCM Algorithm based on K-means and granular computing[J]. Journal of Intelligent Systems,2015,24(2):215-222.
[9]Tzortzis G,Li Kas A,Tzortzis G. The MinMax K-Means clustering algorithm[J]. Pattern Recognition,2014,47(7):2505-2516.
[10]Yu S,Tranchevent L C,Moor B D,et al. Optimized data fusion for Kernel K-means clustering[J]. IEEE Transactions on Software Engineering,2012,34(5):1031-1039.
[11]Zhong C,Malinen M,Miao D,et al. A fast minimum spanning tree algorithm based on K-means[J]. Information Sciences,2015,295(C):1-19.
[12]刘书香,卢才武,张志霞.数据挖掘中的客户聚类分析及其算法实现[J].信息技术,2004(1):4-6,10.
[13]赵文冲,蔡江辉,赵旭俊,等.一种影响空间下的快速K-means聚类算法[J].小型微型计算机系统,2016,37(9):2060-2064.
[14]Na S,Xumin L,Yong G. Research on k-means clustering algorithm:An improved k-means clustering algorithm[C]. Intelligent Information Technology and Security Informations(IITSI),2010 Third International Symposium on. IEEE,2010:63-67.
[2]李春生,王耀南.聚类中心初始化的新方法[J].控制理论与应用,2010,27(10):1435-1440.
[3]邱剑锋.人工蜂群算法的改进方法与收敛性理论的研究[D].合肥:安徽大学,2014:59-63.
[4]雷小锋,谢昆青,林帆,等.一种基于K-Means局部最优性的高效聚类算法[J].软件学报,2008,19(7):1683-1692.
[5]朱颢东,钟勇,赵向辉.一种优化初始中心点的K-Means文本聚类算法[J].郑州大学学报:理学版,2009,41(2):29-32.
[6]陶新民,徐晶,杨立标,等.一种改进的粒子群和K均值混合聚类算法[J].电子与信息学报,2010,32(1):92-97.
[7] LU B,JU F. An optimized genetic K-means clustering algorithm[C]. CSIP 2012:Proceedings of the 2012 International Conference on Computer Science and Information Processing. Piscataway:IEEE,2012:1296-1299.
[8]Lu W J,Yan Z Z. Improved FCM Algorithm based on K-means and granular computing[J]. Journal of Intelligent Systems,2015,24(2):215-222.
[9]Tzortzis G,Li Kas A,Tzortzis G. The MinMax K-Means clustering algorithm[J]. Pattern Recognition,2014,47(7):2505-2516.
[10]Yu S,Tranchevent L C,Moor B D,et al. Optimized data fusion for Kernel K-means clustering[J]. IEEE Transactions on Software Engineering,2012,34(5):1031-1039.
[11]Zhong C,Malinen M,Miao D,et al. A fast minimum spanning tree algorithm based on K-means[J]. Information Sciences,2015,295(C):1-19.
[12]刘书香,卢才武,张志霞.数据挖掘中的客户聚类分析及其算法实现[J].信息技术,2004(1):4-6,10.
[13]赵文冲,蔡江辉,赵旭俊,等.一种影响空间下的快速K-means聚类算法[J].小型微型计算机系统,2016,37(9):2060-2064.
[14]Na S,Xumin L,Yong G. Research on k-means clustering algorithm:An improved k-means clustering algorithm[C]. Intelligent Information Technology and Security Informations(IITSI),2010 Third International Symposium on. IEEE,2010:63-67.