[1]尹成祥 张宏军,张睿,綦秀利,等. 一种改进的K-Means算法[J].计算机技术与发展,2014,24(10):30-33.
　YIN Cheng-xiang,ZHANG Hong-jun,ZHANG Rui,et al. An Improved K-Means Clustering Algorithm[J].,2014,24(10):30-33.
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 一种改进的K-Means算法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
24
期数:
2014年10期
页码:
30-33
栏目:
智能、算法、系统工程
出版日期:
2014-10-10

文章信息/Info

Title:
 An Improved K-Means Clustering Algorithm
文章编号:
1673-629X（2014）10-0030-04
作者:
 尹成祥 张宏军; 张睿; 綦秀利; 王彬
 解放军理工大学
Author(s):
 YIN Cheng-xiang; ZHANG Hong-jun ; ZHANG Rui ; QI Xiu-li; WANG Bin
关键词:
 ; K-Means算法; 分段; 聚类指数; 紧密度; 显著度
Keywords:
 ; K-Means algorithm; segmentation; clustering-index; density; significance
分类号:
TP301.6
文献标志码:
A
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 ]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。
Abstract:
 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.

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更新日期/Last Update: 2015-04-02