[1]邓海,覃华,孙欣.一种优化初始中心的K-means聚类算法[J].计算机技术与发展,2013,(11):42-45.
　DENG Hai,QIN Hua,SUN Xin.A K-means Clustering Algorithm of Meliorated Initial Center[J].,2013,(11):42-45.
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一种优化初始中心的K-means聚类算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年11期
页码:
42-45
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
A K-means Clustering Algorithm of Meliorated Initial Center
文章编号:
1673-629X（2013）11-0042-04
作者:
邓海; 覃华; 孙欣
广西大学 计算机与电子信息学院
Author(s):
DENG Hai; QIN Hua; SUN Xin
关键词:
K-means聚类; 聚类中心; 高密度点; 垂直中心点
Keywords:
K-means clustering; clustering center; high density points; vertical center
文献标志码:
A
摘要:
针对传统K-means聚类算法对初始聚类中心的敏感性和随机性，造成容易陷入局部最优解和聚类结果波动性大的问题，结合密度法和最大化最小距离的思想，提出基于最近高密度点间的垂直中心点优化初始聚类中心的K-means聚类算法。该算法选取相互间距离最大的K对高密度点，并以这K对高密度点的均值作为聚类的初始中心，再进行K-means聚类。实验结果表明，该算法有效排除样本中含有的孤立点，并且聚类过程收敛速度快，聚类结果有更好的准确性和稳定性
Abstract:
The traditional K-means clustering algorithm has the sensitivity and randomness for initial clustering center. So it easily falls in-to local optimal solution and has unstable results. To solve the problem,proposed a K-means algorithm of meliorated initial clustering center based on vertical center point of the closest high density points. This algorithm selects K pairs of high density points that have the maximal distance between each other,and then uses the average values of K pairs of high density points as the initial clustering centers to implement the traditional K-means. The experimental results show that this algorithm is effective to eliminate isolated points and has bet-ter accuracy and stability

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更新日期/Last Update: 1900-01-01