journal6 ›› 2009, Vol. 30 ›› Issue (6): 43-45.
• 计算机 • 上一篇 下一篇
出版日期:
发布日期:
作者简介:
Online:
Published:
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
尹鹏飞, 张晓丹. 一种基于简单遗传算法的K-Means改进算法[J]. journal6, 2009, 30(6): 43-45.
YIN Peng-Fei, ZHANG Xiao-Dan. Improved K-Means Algorithm Based on a Simple Genetic Algorithm[J]. journal6, 2009, 30(6): 43-45.
0 / / 推荐
导出引用管理器 EndNote|Ris|BibTeX
链接本文: https://zkxb.jsu.edu.cn/CN/
https://zkxb.jsu.edu.cn/CN/Y2009/V30/I6/43