计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (18): 136-138.

• 数据库、信号与信息处理 • 上一篇    下一篇

一种基于人工鱼群的混合聚类算法

刘 白,周永权

  

  1. 广西民族大学 数学与计算机科学学院,南宁 530006
  • 收稿日期:2007-09-24 修回日期:2007-12-03 出版日期:2008-06-21 发布日期:2008-06-21
  • 通讯作者: 刘 白

Mixed clustering algorithm based on artificial fish swarm

LIU Bai,ZHOU Yong-quan   

  1. College of Mathematics and Computer Science,Guangxi University for Nationalities,Nanning 530006,China
  • Received:2007-09-24 Revised:2007-12-03 Online:2008-06-21 Published:2008-06-21
  • Contact: LIU Bai

摘要: 聚类分析是数据挖掘的核心技术之一,它是一种无导师监督的模式识别方式。聚类分析就是按照数据间的相似程度,依据特定的准则将数据划分成不同子类。文中通过分析K-平均算法的优缺点,提出了一种基于人工鱼群算法的聚类分析算法,并把它与传统的K-平均算法结合得到一种新的混合聚类算法。仿真实验表明,该算法是有效的,具有聚类速度快、精度高特点。

Abstract: Clustering analysis is one of primary techniques in the field of data mining.It is an unsupervised mode of pattern recognition.Clustering analysis is a division of data into similarity groups according to given rules.K-means algorithm is a classical clustering algorithm.In the paper K-means’ advantage and weakness is analyzed.A novel clustering algorithm based on artificial fish swarm is proposed then a new mixed clustering algorithm is obtained from the combination of K-means and clustering algorithm which is based on artificial fish swarm.The result of the experiment shows it is a kind of efficient algorithm.