模糊c均值聚类算法  

Fuzzy c-Means Clustering Algorithm

作　　者：刘蕊洁[1] 张金波[1] 刘锐[1] 

机构地区：[1]兰州交通大学数理与软件工程学院,兰州730070

出　　处：《重庆工学院学报（自然科学版）》2008年第2期139-141,共3页Journal of Chongqing Institute of Technology

摘　　要：模糊聚类是一种重要数据分析和建模的无监督方法.对模糊聚类进行了概述,从理论和实验2个方面研究了模糊c均值聚类算法,并对该算法的优点及存在的问题进行了分析.结果表明,该算法设计简单,应用范围广,但仍存在容易陷入局部极值点等问题,还需进一步研究.Fuzzy clustering is a powerful unsupervised method for the analysis of data and construction of models. This paper presents an overview of fuzzy clustering and do some study of fuzzy C means clustering algorithm in terms of theory and experiment, analyzes its advantages and existing problems. Results show that this algorithm is simple in design, can be widely used, but there are still some problems in it, and therefore, it is necessary to be studied further.

关 键 词：模糊C均值算法 模糊聚类 聚类分析 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]