journal6 ›› 2009, Vol. 30 ›› Issue (6): 43-45.

• 计算机 • 上一篇    下一篇

一种基于简单遗传算法的K-Means改进算法

  

  1. (1.吉首大学教务处,湖南 吉首 416000,2.吉首大学数计学院,湖南 吉首 416000)
  • 出版日期:2009-11-25 发布日期:2012-04-20
  • 作者简介:尹鹏飞(1978-),男,湖南桃江人,吉首大学助理研究员,主要从事公共计算机教学与高等教育管理研究.

Improved K-Means Algorithm  Based on  a Simple Genetic Algorithm

  1. (1.Office of Education Administration,Jishou University,Jishou 416000,Hunan China;2.College of Mathematics and Computer Science,Jishou University,Jishou 416000,Hunan China)
  • Online:2009-11-25 Published:2012-04-20

摘要:针对k-means算法对初始值敏感、易陷入局部极小值等缺点,结合遗传算法的思想,提出了一种基于遗传算法和k-means算法的混合聚类方法,为了测试该聚类算法的性能,用k-means 算法和改进的算法进行了1组实验,并对2种算法的聚类结果进行比较,实验结果表明算法能够有效地解决聚类问题.

关键词: 数据挖掘, 聚类分析, 遗传算法 K-means算法

Abstract: K-means algorithm is sensitive to initial value,easy to fall into local minimum value.In response to these shortcomings,the idea of genetic algorithm is proposed based on genetic algorithm and k-means algorithm for hybrid clustering method.In order to test the performance of clustering algorithm,a set of experiments are conducted by using k-means algorithm and the improved algorithm,and the clustering results by the two algorithms are compared.It is showed that the clustering algorithm can effectively solve the clustering problem.

Key words: data mining, cluster analysis, genetic algorithm, k-means algorithm

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