一种改进的K-Means算法  

An Improved K-Means Clustering Algorithm

作　　者：尹成祥[1] 张宏军 张睿[1] 綦秀利[1] 王彬[1] 

机构地区：[1]解放军理工大学,江苏南京210007

出　　处：《计算机技术与发展》2014年第10期30-33,共4页Computer Technology and Development

基　　金：国家自然科学基金资助项目(70971137)

摘　　要：针对典型K-Means算法随机选取初始中心点导致的算法迭代次数过多的问题,采取数据分段方法,将数据点根据距离分成k段,在每段内选取一个中心作为初始中心点,进行迭代运算;为寻找最优的聚类数目k,定义了新的聚类有效性函数—聚类指数,包含聚类紧密度和聚类显著度两个指标,通过最优化聚类指数,在[1,n(1/2)]内寻找最优的k值。在IRIS数据集进行的仿真实验结果表明,算法的迭代次数明显减少,寻找的最优k值接近数据集的真实情况,算法有效性得到了验证。Aiming at the problemsof too much iterative times in selecting initial centroids stochastically for K-Means algorithm,a method is proposed to optimize the initial centroids through cutting the set into k segmentations and select one point in each segmentation as initial centroids for iterative computing. A new valid function called clustering-index is defined as the sum of clustering-density and clustering-significance and can be used to search the optimization of k in the internal of [1, n ]. The simulation experiment with IRIS data set shows that the proposed algorithm converges faster and the value k found is close to the actual value,which proves the validity of the al-gorithm.

关 键 词：K-MEANS算法 分段 聚类指数 紧密度 显著度 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]