Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (20): 133-135.DOI: 10.3778/j.issn.1002-8331.2009.20.040

• 数据库、信息处理 • Previous Articles     Next Articles

Research on modified fuzzy C-means clustering algorithm

QI Miao,ZHANG Hua-xiang   

  1. College of Information Science and Engineering,Shandong Normal University,Jinan 250014,China
  • Received:2008-10-09 Revised:2008-12-25 Online:2009-07-11 Published:2009-07-11
  • Contact: QI Miao

改进的模糊C-均值聚类算法研究

齐 淼,张化祥   

  1. 山东师范大学 信息科学与工程学院,济南 250014
  • 通讯作者: 齐 淼

Abstract: The fuzzy C-means clustering algorithm has the shortages including its sensitivity for data of outlier and noise and its distributed imbalanced samples,this paper presents an improved algorithm:By improving the subject function,the impacts of outlier are eliminated,and in order to differentiate the different effects of different samples for knowledge discovery,every sample holds a quantificational weight to improve clustering results of noise and distributed imbalanced samples.The experimental results show that the modified algorithm is more robust and has higher clustering accuracy.

Key words: Fuzzy C-Means(FCM), weights, clustering

摘要: 为解决模糊C-均值(FCM)聚类算法对噪声和孤立点数据敏感、样本分布不均衡的问题,提出了具体的改进和提高的方法:改进隶属度函数,以消除孤立点对聚类结果的影响;为每个样本点赋予一个定量的权值,以区分不同的样本点对于知识发现的不同作用,改善噪音和分布不均衡的样本集的聚类结果。实验结果表明该算法具有更好的健壮性和聚类效果。

关键词: 模糊C-均值, 权值, 聚类