计算机工程与应用 ›› 2013, Vol. 49 ›› Issue (22): 194-196.

• 图形图像处理 • 上一篇    下一篇

一种应用分治策略改进的FCM聚类算法

张思发,刘汭祥   

  1. 中国地质大学(武汉) 计算机学院,武汉 430074
  • 出版日期:2013-11-15 发布日期:2013-11-15

Divide and conquer improved fuzzy C-means clustering method

ZHANG Sifa, LIU Ruixiang   

  1. College of Computer Science and Technology, China University of Geosciences, Wuhan 430074, China
  • Online:2013-11-15 Published:2013-11-15

摘要: 传统的快速聚类算法大多基于模糊C均值算法(Fuzzy C-means,FCM),而FCM对初始聚类中心敏感,对噪音数据敏感并且容易收敛到局部极小值,因而聚类准确率不高。建立使用分治策略解决聚类问题的算法架构,充分考虑数据本身特性并对传统的FCM算法进行改进,标准数据集的实验结果表明这种基于分治策略的FCM聚类算法较好地提高了算法的聚类准确率,加快了收敛速度。

关键词: 模糊C均值聚类, 分治策略, 无监督聚类, 微阵列数据

Abstract: Traditional FCM is sensitive with the initial cluster center and noise also easily converge to a local minimum values, which leads to low clustering accuracy. This article proposes a method that uses divide and conquer technique with equivalency and compatible relation concepts to improve the performance of the FCM clustering method. Experiment results demonstrate appropriate accuracy.

Key words: Fuzzy C-means Clustering(FCM), divide and conquer, unsupervised clustering, micro-array data